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Introducing deep research | OpenAI

February 2, 2025  2025 年 2 月 2 日

Introducing deep research
介紹深度研究

An agent that uses reasoning to synthesize large amounts of online information and complete multi-step research tasks for you. Available to Pro users today, Plus and Team next.
一個能夠運用推理來整合大量線上資訊，並為您完成多步驟研究任務的代理工具。今日提供給專業版用戶，接下來將開放給進階版與團隊版用戶使用。

Try on ChatGPT  在 ChatGPT 上試用(opens in a new window)

Today we’re launching deep research in ChatGPT, a new agentic capability that conducts multi-step research on the internet for complex tasks. It accomplishes in tens of minutes what would take a human many hours.
今天我們在 ChatGPT 推出了深度研究，這是一項新的代理功能，能夠在網路上進行多步驟研究以完成複雜任務。它能在短短幾十分鐘內完成通常需要人類數小時才能完成的工作。

Deep research is OpenAI's next agent that can do work for you independently—you give it a prompt, and ChatGPT will find, analyze, and synthesize hundreds of online sources to create a comprehensive report at the level of a research analyst. Powered by a version of the upcoming OpenAI o3 model that’s optimized for web browsing and data analysis, it leverages reasoning to search, interpret, and analyze massive amounts of text, images, and PDFs on the internet, pivoting as needed in reaction to information it encounters.
深度研究是 OpenAI 的下一代代理工具，能夠獨立為您工作——您只需提供提示，ChatGPT 就會從數百個線上來源中尋找、分析並整合資訊，生成一份研究分析師水準的綜合報告。此工具由即將推出的優化版 OpenAI o3 模型提供支持，該模型專為網頁瀏覽和數據分析設計，能運用推理來搜尋、解讀並分析大量的網路文本、圖片和 PDF，並根據遇到的資訊靈活調整。

The ability to synthesize knowledge is a prerequisite for creating new knowledge. For this reason, deep research marks a significant step toward our broader goal of developing AGI, which we have long envisioned as capable of producing novel scientific research.
整合知識的能力是創造新知識的前提。因此，深度研究標誌著我們邁向更廣泛目標的一個重要步驟，即開發我們長期以來所設想的具備創造新科學研究能力的通用人工智慧（AGI）。

Why we built deep research
為什麼我們開發了深度研究

Deep research is built for people who do intensive knowledge work in areas like finance, science, policy, and engineering and need thorough, precise, and reliable research. It can be equally useful for discerning shoppers looking for hyper-personalized recommendations on purchases that typically require careful research, like cars, appliances, and furniture. Every output is fully documented, with clear citations and a summary of its thinking, making it easy to reference and verify the information. It is particularly effective at finding niche, non-intuitive information that would require browsing numerous websites. Deep research frees up valuable time by allowing you to offload and expedite complex, time-intensive web research with just one query.
深度研究專為從事金融、科學、政策和工程等領域密集知識工作的人士設計，這些工作需要深入、精確且可靠的研究。同時，它對於精明的消費者來說也同樣有用，特別是在購買汽車、家電和家具等需要謹慎研究的商品時，提供高度個性化的建議。每份輸出結果都附有完整的文獻紀錄，包括清晰的引用與思考摘要，方便參考與驗證資訊。它尤其擅長發現需要瀏覽大量網站才能找到的小眾且不直觀的資訊。深度研究讓您能夠通過一次查詢，卸下並加速複雜且耗時的網路研究，節省寶貴的時間。

Deep research independently discovers, reasons about, and consolidates insights from across the web. To accomplish this, it was trained on real-world tasks requiring browser and Python tool use, using the same reinforcement learning methods behind OpenAI o1, our first reasoning model. While o1 demonstrates impressive capabilities in coding, math, and other technical domains, many real-world challenges demand extensive context and information gathering from diverse online sources. Deep research builds on these reasoning capabilities to bridge that gap, allowing it to take on the types of problems people face in work and everyday life.
深度研究能獨立發現、推理並整合來自網路的洞見。為實現這一點，它接受了需要使用瀏覽器和 Python 工具的真實世界任務訓練，並運用了與我們首款推理模型 OpenAI o1 相同的強化學習方法。雖然 o1 在編程、數學和其他技術領域展現了令人印象深刻的能力，但許多現實世界的挑戰需要從不同的線上來源收集大量背景資訊。深度研究在這些推理能力的基礎上進一步發展，填補了這一空白，使其能夠應對人們在工作和日常生活中面臨的各類問題。

How to use deep research
如何使用深度研究

In ChatGPT, select ‘deep research’ in the message composer and enter your query. Tell ChatGPT what you need—whether it’s a competitive analysis on streaming platforms or a personalized report on the best commuter bike. You can attach files or spreadsheets to add context to your question. Once it starts running, a sidebar appears with a summary of the steps taken and sources used.
在 ChatGPT 中，選擇「深度研究」於訊息編輯器中，然後輸入您的問題。告訴 ChatGPT 您的需求，例如對串流平台的競爭分析，或是關於最佳通勤自行車的個性化報告。您可以附加文件或電子表格，為問題提供更多背景。一旦開始執行，側邊欄會顯示所採取的步驟摘要與使用的來源。

Deep research may take anywhere from 5 to 30 minutes to complete its work, taking the time needed to dive deep into the web. In the meantime, you can step away or work on other tasks—you’ll get a notification once the research is complete. The final output arrives as a report within the chat – in the next few weeks, we will also be adding embedded images, data visualizations, and other analytic outputs in these reports for additional clarity and context.
深度研究可能需要 5 到 30 分鐘來完成其工作，花費必要的時間深入網路進行研究。在此期間，您可以離開或處理其他任務——研究完成後，您將收到通知。最終輸出將以報告形式呈現於對話中——在接下來的幾週內，我們還將在這些報告中加入嵌入式圖片、數據視覺化和其他分析輸出，以提供更多清晰度與背景資訊。

Compared to deep research, GPT-4o is ideal for real-time, multimodal conversations. For multi-faceted, domain-specific inquiries where depth and detail are critical, deep research’s ability to conduct extensive exploration and cite each claim is the difference between a quick summary and a well-documented, verified answer that can be usable as a work product.
與深度研究相比，GPT-4o 更適合即時、多模態的對話。而對於需要深度與細節的多面向、專業領域查詢，深度研究能進行廣泛探索並為每項主張提供引述，這是快速摘要與可作為工作成果的經過驗證的答案之間的關鍵差異。

GPT-4o

Do a deep dive into attempts to improve the reprogramming efficiency of OSKM by directly modifying the protein sequences of the four Yamanaka factors. List all relevant papers you find, the authors, the methods used, and the results. Study the patterns in the changes to the proteins and corresponding results across the papers and list the top 3 domains that scientists modify to increase efficiency, and why they believe these changes are effective.
深入探討通過直接修改四個山中因子的蛋白質序列來提升 OSKM 重編程效率的嘗試。列出您找到的所有相關論文、作者、使用的方法和結果。研究各論文中蛋白質變化的模式及其對應結果，並列出科學家為提高效率而修改的前 3 個領域，以及他們認為這些改變有效的原因。

Direct modifications to the protein sequences of the Yamanaka factors—Oct4, Sox2, Klf4, and c-Myc (collectively known as OSKM)—have been explored to enhance the efficiency of somatic cell reprogramming into induced pluripotent stem cells (iPSCs). Below is a summary of relevant studies, including authors, methods, and key findings:
直接修改山中因子（Oct4、Sox2、Klf4 和 c-Myc，合稱 OSKM）的蛋白質序列已被探索用於提高體細胞重編程為誘導性多能幹細胞（iPSCs）的效率。以下是相關研究的摘要，包括作者、方法和關鍵發現：

1. Study on Klf4 Isoforms
1. 關於 Klf4 同工型的研究

  • Authors: Knut Woltjen et al.
    作者：Knut Woltjen 等人。

  • Methods: Investigation of the impact of different Klf4 isoforms on reprogramming efficiency.
    方法：調查不同 Klf4 異構體對重編程效率的影響。

  • Results: The study found that the length of Klf4 influences reprogramming outcomes. Specifically, Klf4 proteins differing by nine amino acids in length, due to alternative start codons, were used in various laboratories. The choice of isoform affected the efficiency of reprogramming, suggesting that specific protein sequence variations in Klf4 can modulate the reprogramming process.
    結果：研究發現，Klf4 的長度會影響重編程的結果。具體而言，由於起始密碼子不同，Klf4 蛋白在不同實驗室中存在九個氨基酸長度的差異。異構體的選擇影響了重編程的效率，這表明 Klf4 特定的蛋白序列變化可以調節重編程過程。

2. Study on Recombinant OSKM Proteins with Polyarginine Tags
2. 帶有多精氨酸標籤的重組 OSKM 蛋白研究

  • Authors: Researchers referenced in "The use of small molecules in somatic-cell reprogramming."
    作者：參考於「小分子在體細胞重編程中的應用」中的研究人員。

  • Methods: Utilization of recombinant OSKM proteins tagged with a cell-penetrant polyarginine sequence to facilitate cellular uptake without genetic modification.
    方法：使用帶有細胞穿透性多精氨酸序列標籤的重組 OSKM 蛋白，促進細胞吸收而無需基因改造。

  • Results: The tagged recombinant proteins successfully entered cells and induced reprogramming, eliminating the need for genetic material transfer. However, this method was noted to be inefficient and required large amounts of recombinant protein, limiting its widespread application.
    結果：帶標籤的重組蛋白成功進入細胞並誘導了重編程，無需轉移基因材料。然而，此方法效率較低，需使用大量重組蛋白，限制了其廣泛應用。

3. Study on Oct4 Variants
3. Oct4 變體研究

  • Authors: Researchers referenced in "Oct-4."
    作者：參考於「Oct-4」中的研究人員。

  • Methods: Comparison of Oct4 with its close relatives, Oct1 and Oct6, in their ability to induce pluripotency.
    方法：比較 Oct4 與其近親 Oct1 和 Oct6 在誘導多能性方面的能力。

  • Results: The study demonstrated that while Sox2, Klf4, and c-Myc could be replaced by their respective family members, Oct4's closer relatives, Oct1 and Oct6, failed to induce pluripotency. This highlights the exclusiveness of Oct4 among POU transcription factors in reprogramming, suggesting that specific sequences within Oct4 are critical for its function in inducing pluripotency.
    結果：研究顯示，儘管 Sox2、Klf4 和 c-Myc 可以被其各自的家族成員替代，但 Oct4 的近親 Oct1 和 Oct6 無法誘導多能性。這突顯了 Oct4 在 POU 轉錄因子中對重編程的獨特作用，表明 Oct4 特定的序列對其誘導多能性的功能至關重要。

Patterns in Protein Modifications and Reprogramming Efficiency
蛋白修飾模式與重編程效率的關聯

Across these studies, certain patterns emerge regarding modifications to the Yamanaka factors and their impact on reprogramming efficiency:
在這些研究中，關於 Yamanaka 因子修飾及其對重編程效率影響的某些模式逐漸浮現：

  • Isoform Selection: The choice of specific protein isoforms, such as the length variants of Klf4, can significantly influence reprogramming outcomes. This suggests that even minor differences in protein sequences can alter the efficiency of inducing pluripotency.
    異構體選擇：特定蛋白異構體的選擇，例如 Klf4 的長度變體，可顯著影響重編程結果。這表明，即使是蛋白序列中的微小差異也可能改變誘導多能性的效率。

  • Protein Tagging: Adding cell-penetrant sequences, like polyarginine tags, to OSKM proteins facilitates their entry into cells without genetic modification. While this approach can induce reprogramming, it often requires high protein concentrations and may suffer from low efficiency.
    蛋白標籤：為 OSKM 蛋白添加細胞穿透性序列（如多精氨酸標籤），可促進其進入細胞而無需基因改造。儘管此方法能誘導重編程，但通常需要高濃度蛋白，且效率較低。

  • Factor Specificity: The unique effectiveness of Oct4, compared to its close relatives Oct1 and Oct6, underscores the importance of specific protein sequences in reprogramming. This indicates that certain domains within these factors are crucial for their function in inducing pluripotency.
    因子特異性：與其近親 Oct1 和 Oct6 相比，Oct4 的獨特效能突顯了特定蛋白序列在重編程中的重要性。這表明這些因子內的某些區域對其誘導多能性的功能至關重要。

Top Three Domains Modified to Increase Reprogramming Efficiency
提高重編程效率的前三大目標區域

Based on the studies and patterns observed, the following domains within the Yamanaka factors are commonly targeted to enhance reprogramming efficiency:
根據這些研究和觀察到的模式，Yamanaka 因子內以下區域通常是增強重編程效率的目標：

1. DNA-Binding Domains:  1. DNA 結合域：

  • Reason for Modification: Altering the DNA-binding domains can enhance the specificity and affinity of the factors for target genes crucial in reprogramming. For instance, selecting specific isoforms of Klf4 that may have variations in their DNA-binding domains can influence reprogramming efficiency.
    修改原因：改變 DNA 結合域可以增強因子對於目標基因的特異性和親和力，這些基因在重編程過程中至關重要。例如，選擇具有 DNA 結合域變異的 Klf4 特定同源物可能會影響重編程的效率。

2. Transactivation Domains:
2. 轉錄激活域：

  • Reason for Modification: Modifying transactivation domains can increase the transcriptional activity of the factors, thereby upregulating genes essential for maintaining pluripotency. Enhancements in these domains can lead to more efficient reprogramming by promoting the expression of target genes.
    修改原因：修改轉錄激活域可以提高因子的轉錄活性，從而上調維持多能性所需的基因表達。這些域的增強可以促進目標基因的表達，進而提高重編程的效率。

3. Nuclear Localization Signals (NLS):
3. 核定位信號（NLS）：

  • Reason for Modification: Enhancing the NLS within these factors ensures their efficient transport into the nucleus, where they exert their function. Improved nuclear localization can increase the availability of these factors at their sites of action, thereby enhancing reprogramming efficiency.
    修改原因：增強這些因子內的核定位信號可確保其有效運輸至細胞核內發揮功能。改進的核定位可提高這些因子在作用位點的可用性，從而增強重編程的效率。

Researchers believe these modifications are effective because they optimize the functional capabilities of the Yamanaka factors, ensuring that they more effectively activate the genetic networks necessary for reprogramming somatic cells into iPSCs.
研究人員認為這些改造之所以有效，是因為它們優化了山中因子的功能能力，確保它們能更有效地激活將體細胞重編程為誘導性多能幹細胞（iPSCs）所需的基因網絡。

Deep research  深度研究

Do a deep dive into attempts to improve the reprogramming efficiency of OSKM by directly modifying the protein sequences of the four Yamanaka factors. List all relevant papers you find, the authors, the methods used, and the results. Study the patterns in the changes to the proteins and corresponding results across the papers and list the top 3 domains that scientists modify to increase efficiency, and why they believe these changes are effective.
深入探討通過直接修改四個山中因子的蛋白質序列來提升 OSKM 重編程效率的嘗試。列出您找到的所有相關論文、作者、使用的方法和結果。研究各論文中蛋白質變化的模式及其對應結果，並列出科學家為提高效率而修改的前 3 個領域，以及他們認為這些改變有效的原因。

Introduction: Cellular reprogramming with Yamanaka’s OSKM factors (Oct4, Sox2, Klf4, c-Myc) is notoriously inefficient (often <0.1% in human fibroblasts). Researchers have therefore engineered these proteins’ sequences to boost their reprogramming activity. Below we review key studies (across mouse, human, and other systems) that directly modified OSKM protein sequences – via domain fusions, point mutations, truncations, or factor substitutions – to enhance iPSC induction. Both successful and unsuccessful modifications are included for a complete perspective. We first list the major papers and then discuss their methods, results, and emerging themes.
引言：使用山中 OSKM 因子（Oct4、Sox2、Klf4、c-Myc）進行的細胞重編程效率極低（在人類成纖維細胞中通常低於 0.1%）。因此，研究人員對這些蛋白質序列進行了設計改造，以提升其重編程活性。以下我們回顧了通過域融合、點突變、截短或因子替換等方式直接修改 OSKM 蛋白質序列以增強 iPSC 誘導的關鍵研究。為提供完整視角，包含了成功與不成功的改造。我們首先列出主要論文，然後討論其方法、結果和新興主題。

Key Studies (Chronological Overview):
關鍵研究（按時間順序概述）：

  1. Takahashi & Yamanaka (Cell, 2006) – “Induction of Pluripotent Stem Cells from Mouse Embryonic and Adult Fibroblast Cultures by Defined Factors.” (Kazutoshi Takahashi, Shinya Yamanaka; Aug 25, 2006). Landmark discovery of OSKM-induced iPSCs (baseline OSKM, no protein modifications).
    Takahashi & Yamanaka（《Cell》，2006）——「通過定義因子從小鼠胚胎和成人成纖維細胞培養物中誘導多能幹細胞。」（Kazutoshi Takahashi, Shinya Yamanaka；2006 年 8 月 25 日）。OSKM 誘導 iPSC 的里程碑式發現（基線 OSKM，無蛋白質改造）。

  2. Nakagawa et al. (PNAS, 2010) – “Promotion of direct reprogramming by transformation-deficient Myc.” (Masato Nakagawa et al.; Aug 10, 2010). Demonstrated that using Myc variants with reduced oncogenicity (L-Myc or c-Myc point mutants) enhances iPSC generation efficiency while minimizing tumorigenicity.
    Nakagawa 等人（《PNAS》，2010）——「通過轉化缺陷型 Myc 促進直接重編程。」（Masato Nakagawa 等人；2010 年 8 月 10 日）。證明使用降低致瘤性的 Myc 變體（L-Myc 或 c-Myc 點突變）可以提高 iPSC 生成效率，同時減少腫瘤形成。

  3. Wang et al. (EMBO Reports, 2011) – “Reprogramming of mouse and human somatic cells by high-performance engineered factors.” (Yang Wang et al.; Apr 2011). Created synthetic OSKM factors fused to the VP16 transactivation domain, achieving dramatically higher reprogramming efficiencies in both mouse and human cells.
    Wang 等人（《EMBO Reports》，2011）——「通過高效能設計因子重編程小鼠和人類體細胞。」（Yang Wang 等人；2011 年 4 月）。創建了融合 VP16 轉錄激活域的合成 OSKM 因子，在小鼠和人類細胞中顯著提升了重編程效率。

  4. Hirai et al. (Stem Cells, 2011) – “Radical acceleration of nuclear reprogramming by chromatin remodeling with the transactivation domain of MyoD.” (Hiroyuki Hirai et al.; 2011). Fused the MyoD activator domain to Oct4, showing faster reprogramming (5% efficiency in MEFs) and improved chromatin opening.
    Hirai 等人（《Stem Cells》，2011）——「通過 MyoD 的轉錄激活域進行染色質重塑，徹底加速核重編程。」（Hiroyuki Hirai 等人；2011 年）。將 MyoD 激活域融合到 Oct4，顯示出更快的重編程（在小鼠胚胎成纖維細胞中效率為 5%）並改善了染色質開啟。

  5. Hirai et al. (PLOS ONE, 2012) – “Efficient iPS Cell Production with the MyoD Transactivation Domain in Serum-Free Culture.” (Hiroyuki Hirai et al.; Mar 30, 2012). Optimized culture conditions for MyoD–Oct4 fusion (“M3O”) achieving ~26% iPSC yield in mouse fibroblasts vs ~2% with wild-type Oct4, and ~7% in human cells vs 1% with OSKM.
    Hirai 等人（《PLOS ONE》，2012）——「在無血清培養中的 MyoD 轉錄激活域實現高效的 iPS 細胞生產。」（Hiroyuki Hirai 等人；2012 年 3 月 30 日）。優化了 MyoD–Oct4 融合蛋白（“M3O”）的培養條件，使小鼠成纖維細胞的 iPSC 產率達到約 26%，相較於野生型 Oct4 的約 2%；在人類細胞中則從 OSKM 的約 1%提升至約 7%。

  6. Hammachi et al. (Cell Reports, 2012) – “Transcriptional activation by Oct4 is sufficient for the maintenance and induction of pluripotency.” (Fella Hammachi et al.; Jul 2012). Showed that Oct4 fused to activator domains (e.g. VP16) can sustain pluripotency; an Oct4-VP16 chimera could replace wild-type Oct4 in reprogramming, indicating Oct4’s activation function is key.
    Hammachi 等人（《Cell Reports》，2012）——「Oct4 的轉錄激活足以維持和誘導多能性。」（Fella Hammachi 等人；2012 年 7 月）。顯示融合激活域（例如 VP16）的 Oct4 可以維持多能性；Oct4-VP16 嵌合體可以取代重編程中的野生型 Oct4，表明 Oct4 的激活功能是關鍵。

  7. Kong et al. (Nucleic Acids Res, 2015) – “Functional interplay between the RK motif and linker segment dictates Oct4–DNA recognition.” (Xiangqian Kong et al.; May 2015). Identified gain-of-function Oct4 point mutants in the DNA-binding domain with ~3-fold higher reprogramming efficiency, by mutating inhibitory residues in Oct4’s POU linker region.
    Kong 等人（《Nucleic Acids Res》，2015）——「RK 基序與連接段之間的功能互作決定了 Oct4 與 DNA 的識別。」（Xiangqian Kong 等人；2015 年 5 月）。通過突變 Oct4 POU 連接區中的抑制性殘基，發現了 DNA 結合域中的 Oct4 增益功能點突變，其重編程效率提高了約 3 倍。

  8. Shah, Narayan, Ptashne et al. (Cell Reports, 2017) – “OCT4 and SOX2 work as transcriptional activators in reprogramming human fibroblasts.” (Shivangi Shah, Santosh Narayan, Mark Ptashne et al.; Aug 2017). Demonstrated that Sox2-VP16 (activator fusion) accelerates and increases human iPSC formation, especially in harder-to-reprogram older donor cells. Conversely, a repressor fusion (Sox2-HP1) abolished iPSC formation.
    Shah、Narayan、Ptashne 等人（《Cell Reports》，2017）——「OCT4 和 SOX2 作為轉錄激活因子在重編程人類成纖維細胞中的作用。」（Shivangi Shah, Santosh Narayan, Mark Ptashne 等人；2017 年 8 月）。證明 Sox2-VP16（激活因子融合蛋白）加速並增加了人類 iPSC 的形成，尤其是在較難重編程的老年供體細胞中。相對地，抑制因子融合蛋白（Sox2-HP1）則完全抑制了 iPSC 的形成。

  9. Hou et al. (Nucleic Acids Res, 2020) – “Concurrent binding to DNA and RNA facilitates the pluripotency reprogramming activity of Sox2.” (Linlin Hou et al.; Apr 2020). Discovered an RNA-binding motif (RBM) in Sox2’s C-terminal domain that is critical for reprogramming. Deleting this 60-aa motif (Sox2-ΔRBM) was an unsuccessful modification, yielding ~36-fold fewer iPSC colonies, underscoring the RBM’s importance.
    Hou 等人（《Nucleic Acids Res》，2020）——「同時結合 DNA 和 RNA 促進了 Sox2 的多能性重編程活性。」（Linlin Hou 等人；2020 年 4 月）。發現了 Sox2 C 末端域中的 RNA 結合基序（RBM），該基序對重編程至關重要。刪除該 60 個氨基酸基序（Sox2-ΔRBM）是一個失敗的改造，導致 iPSC 菌落數量減少約 36 倍，突顯了 RBM 的重要性。

  10. Borisova et al. (iScience, 2022) – “Structurally-discovered KLF4 variants accelerate and stabilize reprogramming to pluripotency.” (Evgeniia Borisova et al.; Jan 2022). Performed alanine-scanning of the Klf4 zinc-finger domain, finding one mutant (Klf4-L507A) that roughly doubled reprogramming efficiency in both mouse and human cells and hastened iPSC colony formation.
    Borisova 等人（《iScience》，2022）——「結構發現的 KLF4 變體加速並穩定了多能性重編程。」（Evgeniia Borisova 等人；2022 年 1 月）。對 Klf4 鋅指域進行了丙氨酸掃描，發現了一個突變（Klf4-L507A），該突變在小鼠和人類細胞中約提高了兩倍的重編程效率，並加速了 iPSC 菌落的形成。

  11. Akifuji et al. (Sci. Reports, 2021) – “MYCL promotes iPSC-like colony formation via MYC Box 0 and 2 domains.” (Chiaki Akifuji et al.; Dec 2021). Probed why L-Myc outperforms c-Myc. Deletions showed Myc’s Box0 and Box2 domains are crucial for efficient reprogramming (loss of either in L-Myc abolished iPSC formation). This explains L-Myc’s greater efficacy and lower oncogenicity compared to c-Myc.
    Akifuji 等人（《Sci. Reports》，2021）——「MYCL 通過 MYC Box 0 和 2 域促進類 iPSC 菌落的形成。」（Chiaki Akifuji 等人；2021 年 12 月）。探討了為何 L-Myc 優於 c-Myc。刪除研究顯示 Myc 的 Box0 和 Box2 域對高效重編程至關重要（L-Myc 中缺失任一域都會導致 iPSC 形成失敗）。這解釋了 L-Myc 相較於 c-Myc 更高的效率及更低的致瘤性。

(The above are representative studies; additional references and details are interwoven in the discussion below.)
（以上為具代表性的研究；更多參考資料與細節已融入以下討論中。）

Protein Modification Strategies & Findings:
蛋白質修飾策略與研究發現：

  • Fusion of Transactivation Domains to Yamanaka Factors: One recurring strategy is to endow OSKM factors with super-activating domains to drive pluripotency genes more strongly. The herpesvirus VP16 acidic activation domain is a common choice – it robustly recruits the transcriptional machinery. Wang et al. (2011) first showed that fusing VP16 to Oct4, Sox2, or Nanog creates “hyperactive” factors that dramatically improve reprogramming. In mouse embryonic fibroblasts (MEFs), replacing Oct4 with Oct4-VP16 increased iPSC colony numbers by ~78-fold (236 vs 3 colonies). Sox2-VP16 gave a ~36-fold boost, and adding Nanog-VP16 further raised efficiency (the 4-factor cocktail with three VP16-fusions yielded 100× more colonies than wild-type OSKM). Remarkably, Oct4–VP16 alone (even without Sox2/Klf4/Myc) was later shown to reprogram MEFs at ~0.2–0.3% efficiency – comparable to the full OSKM cocktail. Similarly, Hammachi et al. (2012) found that tethering strong activators to Oct4 sustains pluripotency: an Oct4–VP16 fusion could maintain ESC self-renewal and induce iPSCs, whereas an Oct4–HP1 (heterochromatin protein 1) fusion, which represses transcription, failed to reprogram cells. These studies established that Oct4 and Sox2 primarily function as transcriptional activators during reprogramming, and amplifying their activation domains greatly increases efficiency.
    將轉錄活化域融合至山中因子：一種常見策略是賦予 OSKM 因子超級活化域，以更強力驅動多能性基因表達。單純皰疹病毒的 VP16 酸性活化域是一個常見的選擇——它能強效招募轉錄機制。Wang 等人（2011 年）首次展示，將 VP16 融合至 Oct4、Sox2 或 Nanog 可以產生「超活化」因子，顯著提升重編程效率。在小鼠胚胎成纖維細胞（MEFs）中，用 Oct4-VP16 取代 Oct4 使得 iPSC 集落數量增加了約 78 倍（236 個對比 3 個集落）。Sox2-VP16 帶來了約 36 倍的提升，加入 Nanog-VP16 則進一步提高效率（含有三個 VP16 融合蛋白的四因子組合比野生型 OSKM 多產生了 100 倍的集落）。值得注意的是，僅使用 Oct4–VP16（即使沒有 Sox2/Klf4/Myc）也能以約 0.2–0.3%的效率重編程 MEFs——這與完整的 OSKM 組合相當。同樣地，Hammachi 等人（2012 年）發現，將強活化因子結合至 Oct4 可以維持多能性：Oct4–VP16 融合蛋白能維持胚胎幹細胞的自我更新並誘導 iPSCs，而 Oct4–HP1（異染色質蛋白 1）融合蛋白，由於抑制轉錄，則無法重編程細胞。這些研究確立了 Oct4 和 Sox2 在重編程過程中主要作為轉錄活化因子，其活化域的增強顯著提高了效率。

    Another successful fusion strategy was to attach domains from myogenic transcription factor MyoD. Hirai et al. (2011, 2012) created an Oct4–MyoD chimera (termed “M_3O”), hypothesizing it would better remodel chromatin. Indeed, M_3O with SKM reprogrammed ~5% of MEFs in 2011. In a follow-up with serum-free, low-density culture, Hirai 2012 achieved 25–30% efficiency with M_3O+SKM – a huge jump from ~2% with wild-type Oct4 (OSKM) under the same conditions. M_3O proved superior to even large VP16 fusions of Oct4, suggesting the MyoD TAD particularly well-suited for opening embryonic gene loci. Notably, M_3O also worked in human fibroblasts (yield ~7% vs 1% for OSKM), showing cross-species efficacy. These fusion approaches consistently indicate that augmenting Oct4/Sox2’s transactivation capability is a powerful way to overcome reprogramming barriers.
    另一個成功的融合策略是將肌生成轉錄因子 MyoD 的結構域附加上去。Hirai 等人（2011, 2012）創造了一個 Oct4–MyoD 嵌合體（稱為“M_3O”），假設它能更有效地重塑染色質。事實上，M_3O 與 SKM 在 2011 年重新編程了約 5%的 MEFs。在後續的無血清、低密度培養條件下，Hirai 於 2012 年使用 M_3O+SKM 達到了 25–30%的效率，這相比於相同條件下野生型 Oct4（OSKM）的~2%是一個巨大的提升。M_3O 甚至比 Oct4 的大型 VP16 融合體更為優越，表明 MyoD TAD 特別適合打開胚胎基因位點。值得注意的是，M_3O 在人體成纖維細胞中也有效（產率約 7%對比 OSKM 的 1%），顯示出跨物種的效能。這些融合方法一致表明，增強 Oct4/Sox2 的轉活化能力是一種克服重編程障礙的強大方式。

    Unsuccessful domain fusions: Not all factors benefit from added domains – outcomes depended on the factor’s role. Wang et al. reported that fusing VP16 to Klf4 or expressing VP16 alone did not enhance reprogramming, implying Klf4’s native function or expression level was not limiting in their system. Likewise, fusing a repressor domain (HP1) to Sox2 completely blocked iPSC formation, underlining that activation (not repression) by Sox2 is required. These controls reinforce that only specific modifications (strong activators to Oct4/Sox2/Nanog) yield efficiency gains, whereas mis‐routing a factor’s function can abolish activity.
    未成功的領域融合：並非所有因子都能從額外的領域中受益——結果取決於因子的角色。Wang 等人報告指出，將 VP16 融合至 Klf4 或僅表達 VP16 並未增強重編程，這意味著 Klf4 的原生功能或表達水平在其系統中並非限制因素。同樣地，將抑制域（HP1）融合至 Sox2 完全阻止了 iPSC 的形成，強調了 Sox2 所需的是激活（而非抑制）。這些對照實驗進一步證實，只有特定的修飾（如強激活因子對 Oct4/Sox2/Nanog）才能提升效率，而錯誤引導因子的功能可能會徹底消除其活性。

  • Point Mutations and Internal Deletions of Domains: A finer approach has been to mutate specific amino acids or domains within OSKM proteins to relieve inhibitory regulation or improve DNA binding. Oct4 (POU5F1) has been a prime target. Oct4 contains a bipartite DNA-binding POU domain (POUs and POUh subdomains separated by a flexible linker) and several regulatory motifs. Kong et al. (2015) discovered that mutations in Oct4’s POU linker can **enhance its DNA binding and reprogramming function. They showed that glutamic acid residues in the linker (e.g. E210/E217 in mouse Oct4) form intramolecular contacts with the DNA-binding “RK motif” (arginine-lysine rich loop) and partially inhibit Oct4’s ability to engage DNA. Disrupting these contacts via alanine substitution (e.g. Oct4-E219A in mouse, corresponding to human E210A) freed Oct4 to bind target genes more avidly. An Oct4 triple mutant (three glutamate-to-alanine changes in the linker) exhibited ~3-fold higher reprogramming efficiency than wild-type. These gain-of-function Oct4 mutants still required co-factors Sox2/Klf4, but significantly increased colony yields. This trend highlights that tuning Oct4’s DNA-interface (either by adding an exogenous TAD or by removing autoinhibitory elements) improves activation of the pluripotency network.
    點突變與區域刪除：一種更精細的方法是對 OSKM 蛋白中的特定氨基酸或區域進行突變，以減輕抑制性調控或提升 DNA 結合能力。Oct4（POU5F1）一直是主要目標。Oct4 包含一個雙組分 DNA 結合 POU 域（POUs 和 POUh 子域，中間由一個靈活的連接子分隔）以及數個調控基序。Kong 等人（2015 年）發現，Oct4 的 POU 連接子中的突變可**增強其 DNA 結合和重編程功能。他們顯示，連接子中的谷氨酸殘基（例如小鼠 Oct4 中的 E210/E217）與 DNA 結合“RK 基序”（精氨酸-賴氨酸富集環）形成分子內接觸，部分抑制了 Oct4 與 DNA 結合的能力。通過丙氨酸替代破壞這些接觸（例如小鼠的 Oct4-E219A，對應於人類的 E210A）使 Oct4 能更積極地結合目標基因。一個 Oct4 三重突變體（連接子中的三個谷氨酸替換為丙氨酸）表現出比野生型高約 3 倍的重編程效率。這些增益功能的 Oct4 突變體仍然需要輔因子 Sox2/Klf4，但顯著提高了克隆產量。這一趨勢突顯了通過調整 Oct4 的 DNA 界面（無論是添加外源性 TAD 或去除自抑制元素）能夠改善多能性網絡的激活。

    Post-translational modification sites on Oct4 have also been targeted. Brumbaugh et al. (PNAS 2012) identified two phosphorylation sites in human OCT4’s DNA-binding homeodomain (T234, S235) whose modification impairs Oct4’s function. They showed that a phospho-mimetic mutant (T234E/S235E) was greatly compromised – it yielded far fewer iPSC colonies than wild-type Oct4 (few colonies formed, indicating loss of function). Conversely, a non-phosphorylatable mutant (T234A/S235A) performed at least as well as wild-type (in some trials modestly better). These results indicate that phosphorylation of Oct4’s homeodomain negatively regulates reprogramming efficiency by reducing DNA binding. Thus, mutating Oct4 to block that phosphorylation can maintain Oct4 in a more active DNA-binding state, benefiting reprogramming (whereas enforcing constant phosphorylation is detrimental).
    Oct4 的翻譯後修飾位點也成為目標。Brumbaugh 等人（PNAS 2012）在人體 OCT4 的 DNA 結合同源域中鑑定出兩個磷酸化位點（T234，S235），其修飾會削弱 Oct4 的功能。他們顯示，一個磷酸模擬突變體（T234E/S235E）功能大幅受損——相比於野生型 Oct4，其誘導的 iPSC 克隆數量少得多（形成的克隆很少，表明功能喪失）。相反，一個不可磷酸化的突變體（T234A/S235A）至少與野生型表現相當（在某些試驗中稍微優於野生型）。這些結果表明，Oct4 同源域的磷酸化會通過減少 DNA 結合來負面調控重編程效率。因此，突變 Oct4 以阻止該磷酸化可以讓 Oct4 保持在更活躍的 DNA 結合狀態，有利於重編程（而強制持續磷酸化則有害）。

    Sox2 has an HMG DNA-binding domain and a C-terminal transactivation region. Intriguingly, a recent finding by Hou et al. (2020) revealed Sox2 also harbors an RNA-binding motif (RBM) in its C-terminus that contributes to reprogramming. Deleting this 60-residue RBM (creating Sox2-ΔRBM) severely reduced iPSC yields – 36-fold fewer colonies relative to wild-type Sox2. The authors showed Sox2’s RBM enables it to bind certain RNAs while still bound to DNA, which facilitates somatic gene silencing and pluripotency gene activation during reprogramming. This was an unsuccessful modification attempt (it impeded reprogramming), but it identifies a new domain critical for Sox2’s reprogramming activity. It cautions that deleting or altering domains without understanding their function can abolish factor activity – in this case, Sox2’s ability to coordinate RNA processing was key to efficient iPSC induction.
    Sox2 具有一個 HMG DNA 結合域和一個 C 端轉錄激活區域。有趣的是，Hou 等人（2020 年）的最新研究發現，Sox2 在其 C 端還包含一個 RNA 結合基序（RBM），該基序對重編程有貢獻。刪除這個 60 個殘基的 RBM（產生 Sox2-ΔRBM）會嚴重降低 iPSC 產量——相比於野生型 Sox2，克隆數量減少了 36 倍。研究人員顯示，Sox2 的 RBM 使其在與 DNA 結合的同時還能結合某些 RNA，這在重編程過程中促進了體細胞基因沉默和多能性基因激活。這是一個不成功的修改嘗試（它阻礙了重編程），但它確定了一個對 Sox2 重編程活性至關重要的新區域。這提醒我們，在不了解功能的情況下刪除或改變區域可能會破壞因子的活性——在這種情況下，Sox2 協調 RNA 處理的能力對於高效的 iPSC 誘導至關重要。

    Klf4 is a zinc-finger (ZnF) transcription factor with three C2H2 ZnFs at its C-terminus responsible for DNA binding. Borisova et al. (2022) performed a systematic mutagenesis of Klf4’s ZnF region to find variants that speed up reprogramming. They identified a single substitution, Klf4-L507A, that markedly improved performance. This leucine at position 507 (in the third zinc finger) was predicted to contact DNA; mutating it to alanine likely alters the protein-DNA interface. In reprogramming assays, Klf4-L507A consistently produced iPSC colonies faster and at roughly double the efficiency of wild-type Klf4. For example, in Nanog-GFP reporter MEFs, ~60% of cells transduced with Klf4-L507A became Nanog+ iPSC colonies by day 25, versus ~30% with wild-type Klf4. This mutant also worked with human fibroblasts to increase TRA-1-60+ pluripotent colony formation. Mechanistically, L507A showed enhanced binding to pluripotency gene promoters (e.g. Klf5) and may stabilize Klf4’s DNA-contact conformation. Interestingly, most other alanine mutants of Klf4 either had no effect or decreased reprogramming (several abolished colony formation entirely). Deletions of entire ZnF domains likewise eliminate Klf4’s ability to induce iPSCs (e.g. a mutant lacking two zinc fingers fails to reprogram). Thus, Klf4’s DNA-binding domain can be finely tuned – one specific change improved efficiency, but many others disrupted the critical DNA contacts. The L507A success underscores how structure-guided mutations can yield “hyperactive” reprogramming factors.
    Klf4 是一種鋅指（ZnF）轉錄因子，在其 C 端有三個 C2H2 鋅指負責與 DNA 結合。Borisova 等人（2022 年）對 Klf4 的鋅指區域進行了系統性突變分析，以尋找能加速重編程的變異體。他們發現了一個單一取代突變 Klf4-L507A，顯著提升了性能。位於第 507 位的亮氨酸（在第三個鋅指中）預測會與 DNA 接觸；將其突變為丙氨酸可能改變蛋白質與 DNA 的界面。在重編程實驗中，Klf4-L507A 穩定且快速地生成 iPSC 菌落，其效率約為野生型 Klf4 的兩倍。例如，在 Nanog-GFP 報導基因的 MEFs 中，約 60%的細胞在第 25 天經 Klf4-L507A 轉導後成為 Nanog+的 iPSC 菌落，而野生型 Klf4 僅約 30%。此外，該突變體也能有效促進人類纖維母細胞生成 TRA-1-60+的多能性菌落。在機制上，L507A 表現出對多能性基因啟動子（如 Klf5）的增強結合，並可能穩定 Klf4 與 DNA 接觸的構象。有趣的是，大多數其他 Klf4 的丙氨酸突變要麼無影響，要麼降低重編程效率（其中幾個完全抑制了菌落的生成）。刪除整個鋅指區域同樣會消除 Klf4 誘導 iPSCs 的能力（例如，缺少兩個鋅指的突變體無法進行重編程）。因此，Klf4 的 DNA 結合區域可以被精細調控——一個特定的改變提升了效率，但許多其他改變則破壞了關鍵的 DNA 接觸。L507A 的成功突顯了結構導向的突變如何產生“超活性”的重編程因子。

  • Modifying c-Myc for Enhanced & Safer Reprogramming: The proto-oncogene c-Myc boosts reprogramming efficiency ~10–40×, but its use raises the risk of tumorigenesis. Researchers have tried altering Myc’s protein sequence to retain reprogramming function while reducing its cancerous potential. Nakagawa et al. (2010) made the seminal observation that L-Myc (the less oncogenic Myc family member) can substitute for c-Myc to promote iPSC generation more efficiently and specifically than c-Myc. In human fibroblasts, L-Myc gave higher iPSC yields than c-Myc, and in mouse chimeras L-Myc did not induce tumors whereas c-Myc did. They also tested c-Myc mutants defective in transformation: notably W136E (a point mutation in Myc’s Myc Box II domain) and ΔN2 (deletion of Myc’s N-terminal Myc Box II segment). These mutants have little transforming activity but Nakagawa et al. found they still enhanced iPSC formation – in fact, they promoted human iPSC colonies more efficiently than wild-type c-Myc. This striking result showed that Myc’s reprogramming function can be uncoupled from its oncogenic function. Mechanistically, c-Myc’s N-terminal Myc Box domains (MBI and MBII) drive proliferation and tumorigenesis, whereas apparently the same or overlapping regions also aid in resetting cell identity; the transformation-deficient mutants presumably still activate the proliferation/apoptosis genes needed to facilitate reprogramming, but without triggering neoplastic programs. These findings led to widespread adoption of L-Myc in iPSC protocols to avoid using c-Myc.
    修飾 c-Myc 以增強與更安全的重編程：原癌基因 c-Myc 可將重編程效率提升約 10–40 倍，但其使用會增加腫瘤發生的風險。研究人員嘗試改變 Myc 的蛋白質序列，以保留其重編程功能，同時降低其致癌潛能。Nakagawa 等人（2010 年）首次發現 L-Myc（Myc 家族中致癌性較低的成員）可以取代 c-Myc，更有效且更專一地促進 iPSC（誘導性多能幹細胞）的生成。在人類成纖維細胞中，L-Myc 產生的 iPSC 效率高於 c-Myc，而在小鼠嵌合體中，L-Myc 並未誘發腫瘤，而 c-Myc 則會。此外，他們還測試了改變轉化功能的 c-Myc 突變體：特別是 W136E（Myc 的 Myc Box II 結構域中的點突變）和 ΔN2（Myc 的 N 端 Myc Box II 區段的缺失突變）。這些突變體的轉化活性較低，但 Nakagawa 等人發現它們仍能促進 iPSC 的形成——實際上，它們比野生型 c-Myc 更有效地促進了人類 iPSC 集落的生成。這一驚人的結果表明，Myc 的重編程功能可以與其致癌功能分離開來。在機制上，c-Myc 的 N 端 Myc Box 結構域（MBI 和 MBII）驅動細胞增殖和腫瘤形成，而顯然相同或重疊的區域也有助於細胞身份的重置；這些缺乏轉化功能的突變體可能仍能激活促進重編程所需的增殖/凋亡基因，但不會觸發腫瘤形成的程序。這些發現導致 L-Myc 在 iPSC 協議中的廣泛應用，以避免使用 c-Myc。

    Building on that, Akifuji et al. (2021) dissected why L-Myc outperforms c-Myc by systematically deleting conserved Myc Box (MB) domains. Myc proteins have six MB domains (MB0–MB5) that mediate protein–protein interactions. Akifuji’s team compared reprogramming ability of c-Myc vs L-Myc mutants in human cells. They found two domains – MB0 and MB2 – are absolutely required for efficient iPSC induction, especially in L-Myc. Deleting MB0 or MB2 from L-Myc completely abrogated colony formation (no iPSCs at all), whereas deleting other domains had milder effects. In c-Myc, loss of MB2 was also detrimental, but loss of MB0 was more tolerated (unlike in L-Myc). This suggests L-Myc’s superior performance stems from functional differences in how its MB0 operates. Proteomic analysis showed L-Myc’s MB0 domain uniquely upregulates cell-adhesion and MET (mesenchymal-to-epithelial transition) related genes, aiding the early reprogramming switch. MB2, on the other hand, helps recruit RNA-processing factors in both c-Myc and L-Myc, impacting protein synthesis and cell growth during reprogramming. Thus, the MB0 and MB2 domains emerged as key effectors of Myc-driven reprogramming efficiency. These insights explain why L-Myc (which has an intact MB0 and MB2 but is less oncogenic) is a sweet spot: it triggers proliferation and MET processes helpful for reprogramming, without strongly activating the tumorigenic pathways tied to c-Myc. From a protein-engineering perspective, one could imagine designing a c-Myc variant that mimics L-Myc’s domain functions – effectively leveraging MB0/MB2 while disabling the pro-tumor elements of MB1/MBII – as an optimal reprogramming factor. Indeed, the 2010 study’s c-Myc-W136E mutant in MBII is one example of such a partial decoupling.
    在此基礎上，Akifuji 等人（2021）透過系統性刪除保守的 Myc Box（MB）區域，剖析了為何 L-Myc 的表現優於 c-Myc。Myc 蛋白具有六個 MB 區域（MB0–MB5），負責調控蛋白質間的交互作用。Akifuji 團隊比較了 c-Myc 與 L-Myc 突變體在人類細胞中的重編程能力。他們發現，有兩個區域——MB0 和 MB2——對於有效的 iPSC 誘導是絕對必要的，尤其是在 L-Myc 中。刪除 L-Myc 的 MB0 或 MB2 會完全阻斷集落形成（完全無法產生 iPSCs），而刪除其他區域則影響較小。在 c-Myc 中，缺失 MB2 也會有不利影響，但缺失 MB0 的容忍度較高（與 L-Myc 不同）。這表明 L-Myc 的卓越表現源於其 MB0 在功能上的差異。蛋白質組學分析顯示，L-Myc 的 MB0 區域能獨特地上調與細胞黏附和 MET（間葉上皮轉化）相關的基因，促進早期重編程的轉換。另一方面，MB2 協助招募 RNA 處理因子，這在 c-Myc 和 L-Myc 中均有作用，影響重編程過程中的蛋白質合成及細胞生長。因此，MB0 和 MB2 區域被認為是驅動 Myc 重編程效率的關鍵效應因子。這些研究結果解釋了為何 L-Myc（擁有完整的 MB0 和 MB2，但致癌性較低）是一個理想的選擇：它能啟動有助於重編程的增殖與 MET 過程，但不會強烈激活與 c-Myc 相關的腫瘤生成途徑。從蛋白質工程的角度來看，可以設計一種模仿 L-Myc 區域功能的 c-Myc 變體——有效利用 MB0/MB2 的同時，抑制 MB1/MBII 的促腫瘤元素——作為最佳的重編程因子。事實上，2010 年研究中的 c-Myc-W136E 突變體（位於 MBII 區域）就是此類部分功能分離的例子之一。

    Other Myc modifications: Some groups have simply omitted c-Myc entirely (to avoid its risks), but this typically drops efficiency ~10–100 fold. Others have tried small-molecule Myc replacements or regulated Myc expression, but those fall outside protein sequence changes. The L-Myc swap and MBII mutants remain the principal protein-level tweaks to the Myc factor itself that improved the reprogramming outcome.
    其他 Myc 修改：有些團隊乾脆完全省略了 c-Myc（以避免其風險），但這通常會使效率下降約 10–100 倍。另一些則嘗試使用小分子 Myc 替代物或調控 Myc 表達，但這些並不屬於蛋白質序列的改變。L-Myc 替換與 MBII 突變仍然是針對 Myc 因子本身進行的主要蛋白質層級調整，能夠改善重編程的結果。

Trends and Effective Strategies: Clear patterns emerge from these studies. A major theme is that increasing the transcriptional activating power of the core pluripotency factors (Oct4, Sox2, Nanog) yields more iPSCs. Whether by fusing potent activation domains (VP16, MyoD TAD) or by removing inhibitory phosphosites and autoinhibitory contacts, the goal is to drive endogenous pluripotency genes (Oct4, Nanog, etc.) more strongly and swiftly. This often translates to higher and earlier activation of the pluripotent gene network, giving more colonies in a shorter time. For example, VP16-fused factors reactivate silent pluripotency loci faster than wild-type factors. Thus, enhanced transactivation is a recurring successful strategy. Alongside this, optimizing DNA-binding interactions of OSKM is another fruitful approach – Oct4 and Klf4 mutants that bind target sites with higher affinity or specificity produce more iPSC colonies. In essence, making these factors better at finding and turning on the right genes improves reprogramming.
趨勢與有效策略：從這些研究中可以看出清晰的模式。主要主題之一是增加核心多能性因子（Oct4、Sox2、Nanog）的轉錄活化能力能夠產生更多的 iPSCs。無論是通過融合強效的活化域（如 VP16、MyoD TAD），還是移除抑制性磷酸化位點和自抑制接觸，其目標都是更強而快速地驅動內源性多能性基因（如 Oct4、Nanog 等）。這通常表現為多能性基因網絡的更高且更早的激活，從而在較短時間內產生更多的集落。例如，與野生型因子相比，VP16 融合因子能更快地重新激活沉默的多能性基因座。因此，增強轉錄活化是一項反覆成功的策略。與此同時，優化 OSKM 的 DNA 結合互動也是另一個有效的方法——如 Oct4 和 Klf4 突變體，其對目標位點的結合親和力或特異性更高，能產生更多的 iPSC 集落。簡而言之，讓這些因子更善於找到並啟動正確的基因，有助於提高重編程效率。

Another trend is modulating factor domains to separate “good” reprogramming functions from “bad” side effects. This is exemplified by c-Myc: modifications that drop Myc’s oncogenic transformation activity (e.g. removing MBII) while keeping its pro-proliferation activity enhance reprogramming and safety. Likewise, one Oct4 mutant (Oct4-E208A) identified by Kong et al. showed higher reprogramming efficiency without disrupting other necessary protein–protein interactions, essentially a cleaner activation function. In contrast, some modifications taught us what not to change: e.g., deleting Sox2’s RBM crippled reprogramming, indicating that domain’s positive role; similarly, certain Klf4 ZnF mutants abolished activity, revealing those residues are indispensable. Including such “unsuccessful” attempts in the analysis helps pinpoint which domains are absolutely required versus which can be engineered for gain of function.
另一個趨勢是調節因子區域以分離「良性」重編程功能與「不良」副作用的辦法。這在 c-Myc 上得到了體現：透過修改來去除 Myc 的致癌轉化活性（例如移除 MBII），同時保留其促進增殖的活性，這樣不僅提高了重編程效率，也增強了安全性。同樣地，Kong 等人鑑定出的 Oct4 突變體（Oct4-E208A）顯示出更高的重編程效率，且未干擾其他必要的蛋白質–蛋白質交互，基本上實現了一種更純淨的激活功能。相比之下，一些修改教會我們哪些不應該改變：例如，刪除 Sox2 的 RBM 會嚴重削弱重編程，表明該區域具有正面作用；類似地，某些 Klf4 ZnF 突變體會導致活性喪失，揭示這些殘基是不可或缺的。在分析中包含這些「不成功」的嘗試有助於確定哪些區域是絕對必要的，哪些則可以通過工程化實現功能增強。

Most Frequently Targeted Domains: From the collective data, three protein regions stand out as the most commonly modified in efforts to boost OSKM function:
最常被目標化的區域：從集體數據來看，有三個蛋白質區域在提升 OSKM 功能的努力中最為常見地被修改：

  1. Transactivation Domains (TADs) – While OSKM factors have their own activation domains, researchers frequently add heterologous TADs to amplify their effect. The viral VP16 TAD is the prototypical example, used in multiple studies for Oct4, Sox2, Nanog fusions. The MyoD TAD (a strong acidic activator from a muscle TF) is another, used in Hirai’s Oct4-M_3O. These domains recruit co-activators (p300/CBP, Mediator, etc.) en masse, thus modified factors can robustly turn on pluripotency genes that somatic chromatin normally silences. TAD fusion is effective because it bypasses some rate-limiting steps in epigenetic activation – for instance, Oct4-VP16 can activate endogenous OCT4 and NANOG loci at lower protein levels than wild-type Oct4. In summary, VP16 and other exogenous TADs are popular and effective additions to OSKM, often yielding order-of-magnitude improvements in efficiency (e.g. 10–100× more colonies). The success of this strategy across species (mouse and human) in multiple labs makes TAD fusion arguably the most impactful modification type discovered.
    轉錄激活區域（TADs）——雖然 OSKM 因子本身具有激活區域，研究者經常加入異源 TADs 以放大其效果。病毒 VP16 TAD 是典型例子，在多項研究中被用於 Oct4、Sox2、Nanog 融合蛋白中。另一例子是 MyoD TAD（來自肌肉轉錄因子的強酸性激活器），Hirai 將其用於 Oct4-M_3O 中。這些區域能大規模招募共激活因子（如 p300/CBP、Mediator 等），因此修改後的因子能夠強力啟動體細胞染色質通常沉默的多能性基因。TAD 融合之所以有效，是因為它繞過了一些表觀遺傳激活的速率限制步驟——例如，Oct4-VP16 能在比野生型 Oct4 更低的蛋白質水平下激活內源性 OCT4 和 NANOG 基因座。總而言之，VP16 及其他外源 TADs 是 OSKM 中受歡迎且有效的添加部分，經常能帶來數量級的效率提升（例如多出 10–100 倍的菌落）。這種策略在多個實驗室中跨物種（小鼠和人類）的成功，讓 TAD 融合無疑成為迄今發現的最具影響力的修改類型之一。

  2. DNA-binding Domains and Linkers (Oct4 POU domain & Klf4 zinc fingers): The portions of OSKM that contact DNA – and their adjacent regulatory motifs – are another frequent focus. Oct4’s POU domain has been engineered in two ways: (i) Surface mutations in the minor-groove binding RK motif or its linker, which increased DNA-binding affinity and yielded more efficient reprogramming; and (ii) Preventing inhibitory modifications (like the Oct4 T234/S235 phosphorylation in the homeodomain) to keep DNA binding robust. These modifications target the “core business” of Oct4 as a transcription factor: by ensuring Oct4 binds the right genes more avidly (either via structural changes or by removing a phosphate that would weaken binding), the cell’s fate can be reset more easily. Klf4’s zinc-finger domain is analogous – the KLF4–L507A mutant alters the interface of the third ZnF, apparently allowing tighter or more prolonged binding at pluripotent gene promoters. This mutant doubled iPSC yields and even made resulting iPSCs more homogeneous in Nanog expression, suggesting a qualitative improvement in reprogramming. In general, mutating DNA-contact residues (positively in Oct4 and Klf4’s case) or domain swaps between POU factors (e.g. Oct4 vs. the less effective Oct6, as examined in some studies) have been common experiments. The recurring outcome is that relatively small changes in DNA-binding domains can have outsized effects on reprogramming efficiency – likely because binding site affinity/specificity is a key determinant of how effectively OSKM can activate endogenous pluripotency genes.
    DNA 結合區域與連接區域（Oct4 POU 區域與 Klf4 鋅指區域）：OSKM 中與 DNA 接觸的部分及其相鄰的調控基序是另一個常見的研究重點。Oct4 的 POU 區域已被以兩種方式進行工程改造：（i）在次溝結合的 RK 基序或其連接區域進行表面突變，這增強了 DNA 結合能力並提高了重編程效率；以及（ii）防止抑制性修飾（例如在同源域中的 Oct4 T234/S235 磷酸化）以保持 DNA 結合的穩定性。這些改造針對的是 Oct4 作為轉錄因子的「核心功能」：透過確保 Oct4 能更穩定地與正確的基因結合（無論是透過結構改變還是移除會削弱結合的磷酸基），使細胞命運更容易被重置。Klf4 的鋅指區域與之類似——KLF4-L507A 突變改變了第三鋅指的界面，顯然能在多能性基因啟動子上實現更緊密或更持久的結合。這種突變使 iPSC 產量翻倍，甚至使所得的 iPSC 在 Nanog 表達上更加均一，顯示出重編程質量的提升。總體來說，對 DNA 接觸殘基的突變（在 Oct4 和 Klf4 的情況下是正向的）或 POU 因子之間的區域交換（例如 Oct4 與效能較低的 Oct6 之間的交換，某些研究有探討）是常見的實驗。反覆的結果顯示，DNA 結合區域的相對較小改變可能對重編程效率產生巨大的影響——這可能是因為結合位點的親和力/特異性是 OSKM 如何有效激活內源性多能性基因的關鍵決定因素。

  3. Myc’s N-terminal Myc Box Domains (especially MB0 and MBII): For c-Myc (and its variant L-Myc), the Myc Box regions in the transactivation domain are the critical modulators of reprogramming versus transformation. Many studies (Nakagawa 2010, Akifuji 2021) converged on MBII (Myc Box II) as necessary for reprogramming promotion, but also responsible for Myc’s oncogenic effects. The point mutation W136E in MBII disrupts interaction with histone acetyltransferase complexes (TRRAP/p400) – this ablates transformation but still permits reprogramming aid. Meanwhile, MB0 (Myc Box 0, a lesser-known domain at the extreme N-terminus) emerged as another major contributor in L-Myc. MB0 differs slightly between c- vs L-Myc, and Akifuji et al. found it influences cell-adhesion gene expression and MET during reprogramming. Because MB0 and MBII (MB2) are “hotspots” for modifying Myc’s behavior, they are the most frequently altered domains in Myc engineering studies. Researchers either swap the whole factor (c-Myc → L-Myc, effectively changing the MB0/MBII context), or create specific mutants/deletions in these domains. The effectiveness of these modifications is measured not just by colony count but also by quality and safety of iPSCs. For instance, using L-Myc (with an intact MB0 but inherently attenuated oncogenicity) yielded iPSC lines that contributed to mouse germline with no tumors. Thus, targeting Myc’s MB domains has been an effective strategy to improve the risk-benefit ratio of reprogramming – maintaining high efficiency while mitigating cancer risk.
    Myc 的 N 端 Myc Box 區域（特別是 MB0 與 MBII）：對於 c-Myc（及其變體 L-Myc），轉錄活化區域中的 Myc Box 區域是調節重編程與轉化的關鍵。許多研究（如 Nakagawa 2010，Akifuji 2021）都聚焦於 MBII（Myc Box II），認為其對促進重編程是必要的，但也與 Myc 的致癌作用相關。在 MBII 中的點突變 W136E 會破壞其與組蛋白乙醯轉移酶複合體（TRRAP/p400）的相互作用——這會消除其轉化能力，但仍能支持重編程。同時，MB0（Myc Box 0，位於最 N 端的一個較少被了解的區域）則成為 L-Myc 中的另一重要貢獻者。MB0 在 c-Myc 與 L-Myc 之間略有不同，Akifuji 等人發現它會影響重編程過程中的細胞黏附基因表達及 MET。由於 MB0 與 MBII（MB2）是改變 Myc 行為的「熱點」，它們成為 Myc 工程研究中最常被改變的區域。研究人員要麼交換整個因子（c-Myc → L-Myc，實際上改變了 MB0/MBII 的背景），要麼在這些區域創建特定的突變/刪除。這些改造的效果不僅以集落數量來衡量，還包括 iPSC 的質量與安全性。例如，使用 L-Myc（MB0 完整但固有的致癌性減弱）生成的 iPSC 系能貢獻於小鼠生殖系統且無腫瘤。因此，針對 Myc 的 MB 區域已成為一種有效的策略，能改善重編程的風險效益比——在維持高效率的同時減輕癌症風險。

Cross-Species and Additional Context: Most modifications above were tested in mouse and human cells, with broadly similar results. VP16 fusions worked in both species (though absolute efficiencies differ). MyoD–Oct4 was shown in mouse and partially in human. Myc’s L-Myc advantage was demonstrated in mouse (chimeras) and human fibroblasts. Klf4-L507A was functionally validated in mouse MEFs and human cells (fibroblasts and keratinocytes). This cross-species consistency suggests these protein-level tweaks tap into fundamental mechanisms of the reprogramming process. It’s also worth noting that older studies laid the groundwork by showing what wild-type OSKM can and cannot do (e.g. the requirement for Oct4 and Sox2 as activators, the severe efficiency drop without c-Myc). Building on that, newer advancements use modern tools (structural modeling, alanine scanning, phosphoproteomics, etc.) to rationally design improved factor versions.
跨物種與額外背景：以上大多數改造已在小鼠與人類細胞中進行測試，並且結果大致相似。VP16 融合蛋白在兩種物種中均有效（儘管絕對效率有所不同）。MyoD–Oct4 在小鼠中顯示效果，在人類中則部分有效。Myc 的 L-Myc 優勢已在小鼠（嵌合體）與人類成纖維細胞中得到證實。Klf4-L507A 已在小鼠 MEF 與人類細胞（成纖維細胞和角質形成細胞）中進行功能驗證。這種跨物種的一致性表明，這些蛋白質層級的改造觸及了重編程過程的基本機制。同時值得注意的是，早期研究已奠定了基礎，展示了野生型 OSKM 能與不能完成的任務（例如，Oct4 和 Sox2 作為激活因子的必要性，以及缺少 c-Myc 時效率的嚴重下降）。基於此，較新的進展利用現代工具（結構建模、丙氨酸掃描、磷酸蛋白組學等）來合理設計改良的因子版本。

Conclusion: In summary, a wealth of research has attempted to “turbo-charge” the Yamanaka factors by altering their protein sequences. Successful approaches include appending strong activation domains (e.g. VP16, MyoD TAD) to Oct4/Sox2, introducing point mutations in Oct4 or Klf4 that strengthen DNA binding or prevent inhibitory phosphorylation, and using Myc variants or mutants that dissociate reprogramming potency from tumorigenic risk. These modifications have yielded quantitative gains in reprogramming efficiency – often boosting iPSC yields from well below 0.5% into the several-percent range or higher, and in special cases (MyoD–Oct4) into double digits. Just as importantly, studying both effective and ineffective modifications has illuminated which protein domains are pivotal for reprogramming. The top three targeted domains have been: (1) exogenous transactivation domains like VP16 (to amplify transcriptional activation); (2) the core DNA-binding domains of pioneer factors (Oct4’s POU and Klf4’s ZnF, including their regulatory linker motifs); and (3) the Myc Box domains of c-Myc/L-Myc (which govern the balance between proliferation and transformation). Modifications in these regions are believed to work by either unlocking latent activity of the factors (e.g. relieving autoinhibition or recruiting more co-factors) or by disconnecting undesired functions (e.g. oncogenesis) from the reprogramming process.
結論：總結來說，大量研究嘗試通過改變蛋白質序列來「加速」山中因子。成功的方法包括將強活化區域（如 VP16，MyoD TAD）附加到 Oct4/Sox2，對 Oct4 或 Klf4 進行點突變以增強 DNA 結合或防止抑制性磷酸化，並使用 Myc 變體或突變體以將重編程能力與腫瘤風險分離開來。這些改造在重編程效率上帶來了定量的提升——通常將 iPSC 產量從不到 0.5%提升到幾個百分點甚至更高，在特殊情況下（如 MyoD–Oct4）甚至達到兩位數。同樣重要的是，研究有效與無效的改造都揭示了哪些蛋白質區域對於重編程至關重要。最常被針對的三大區域是：（1）外源性活化區域如 VP16（用於增強轉錄激活）；（2）先驅因子的核心 DNA 結合區域（Oct4 的 POU 和 Klf4 的鋅指，包括它們的調控連接基序）；以及（3）c-Myc/L-Myc 的 Myc Box 區域（調節增殖與轉化的平衡）。這些區域的改造被認為是通過釋放因子的潛在活性（如解除自我抑制或招募更多輔助因子）或將不需要的功能（如致癌性）與重編程過程分離達成的。

The trajectory from older landmark findings to recent advances shows a clear maturation of the field: from initially swapping whole factors (c-Myc to L-Myc) to domain-level engineering and precise amino-acid substitutions. Each iterative improvement has taught us more about the biology of reprogramming. While no single “super-factor” has yet made reprogramming routine, the compiled evidence makes it clear that the efficiency ceiling can be raised by protein engineering. Combining multiple enhancements – for example, using an Oct4-VP16 + Sox2-VP16 + Klf4-L507A + L-Myc cocktail – might synergistically produce even greater efficiency, an idea some studies have begun to explore (Wang 2011’s use of three synthetic factors was a step in this direction). In closing, direct modifications of OSKM proteins have proven to be a fertile strategy for improving iPSC technology, offering both practical benefits (faster, higher-yield reprogramming) and deeper insight into the structure-function relationships that underlie cell-fate conversion.
從早期重要發現到近期進展的軌跡顯示了該領域的明顯成熟：從最初交換整個因子（如 c-Myc 到 L-Myc）到區域層級的工程設計以及精確的氨基酸替換。每一次迭代的改進都讓我們對重編程生物學有了更多了解。雖然尚未有單一的「超級因子」能使重編程變得常規化，但現有的證據清楚表明，通過蛋白質工程可以提高效率上限。結合多種改進——例如使用 Oct4-VP16 + Sox2-VP16 + Klf4-L507A + L-Myc 的組合——可能會產生更大的協同效應，一些研究已開始探索這一想法（如 Wang 2011 中使用三種合成因子的研究是朝這個方向邁出的一步）。總之，對 OSKM 蛋白的直接改造已被證明是一種富有成效的策略，不僅能提升 iPSC 技術的實用性（更快、更高產的重編程），還能深入了解細胞命運轉換背後的結構-功能關係。

Sources:  資料來源：

  • Takahashi et al., 2006. Cell 126(4):663-76. PMID 16904174.
    Takahashi 等，2006 年。《Cell》126(4):663-76。PMID 16904174。

  • Nakagawa et al., 2010. PNAS 107(32):14152-7.
    Nakagawa 等，2010 年。《PNAS》107(32):14152-7。

  • Wang et al., 2011. EMBO Rep 12(4):373-8.
    Wang 等，2011 年。《EMBO Rep》12(4):373-8。

  • Hirai et al., 2011. Stem Cells 29(8):1349-61.
    Hirai 等，2011 年。《Stem Cells》29(8):1349-61。

  • Hirai et al., 2012. PLoS ONE 7(3):e34149.
    Hirai 等，2012 年。《PLoS ONE》7(3):e34149。

  • Hammachi et al., 2012. Cell Rep 1(2):99-109.
    Hammachi 等，2012 年。《Cell Rep》1(2):99-109。

  • Kong et al., 2015. Nucleic Acids Res 43(9):4381-92.
    Kong 等，2015 年。《Nucleic Acids Res》43(9):4381-92。

  • Shah (Narayan) et al., 2017. Cell Rep 21(8):2100-2116.
    Shah（Narayan）等，2017 年。《Cell Rep》21(8):2100-2116。

  • Hou et al., 2020. Nucleic Acids Res 48(7):3869-3884.
    Hou 等，2020 年。《Nucleic Acids Res》48(7):3869-3884。

  • Borisova et al., 2022. iScience 25(1):103525.
    Borisova 等，2022 年。《iScience》25(1):103525。

  • Akifuji et al., 2021. Sci Rep 11:24254.
    Akifuji 等，2021 年。《Sci Rep》11:24254。

Deep research provides a comprehensive literature review with specific citations, explains mechanistic insights for each domain modification, and addresses all elements of the prompt (papers, authors, methods, results, patterns, top domains, and the rationale for effectiveness) in clear detail.
深度研究提供了全面的文獻回顧，包含具體的引文，詳細解釋了每個區域改變的機制性見解，並清楚地涵蓋了所有提示的要素（論文、作者、方法、結果、模式、主要區域及有效性原理）。

How it works  運作方式

Deep research was trained using end-to-end reinforcement learning on hard browsing and reasoning tasks across a range of domains. Through that training, it learned to plan and execute a multi-step trajectory to find the data it needs, backtracking and reacting to real-time information where necessary. The model is also able to browse over user uploaded files, plot and iterate on graphs using the python tool, embed both generated graphs and images from websites in its responses, and cite specific sentences or passages from its sources. As a result of this training, it reaches new highs on a number of public evaluations focused on real-world problems.
深度研究透過針對多領域的困難瀏覽與推理任務進行端到端強化學習訓練。通過這種訓練，它學會了規劃並執行多步驟的路徑以尋找所需數據，並在必要時回溯與回應即時資訊。該模型還能瀏覽用戶上傳的文件，使用 Python 工具繪製並迭代圖表，將生成的圖表與來自網站的圖像嵌入回應中，並引用來源中的特定句子或段落。經過此訓練後，它在多項聚焦於真實世界問題的公開評估中達到新高。

Humanity's Last Exam  人類的最後考驗

On Humanity’s Last Exam⁠(opens in a new window), a recently released evaluation that tests AI across a broad range of subjects on expert-level questions, the model powering deep research scores a new high at 26.6% accuracy. This test consists of over 3,000 multiple choice and short answer questions across more than 100 subjects from linguistics to rocket science, classics to ecology. Compared to OpenAI o1, the largest gains appeared in chemistry, humanities and social sciences, and mathematics. The model powering deep research showcased a human-like approach by effectively seeking out specialized information when necessary.
在《人類的最後考驗》——一項最近發布的評估中，測試 AI 在廣泛主題上的專家級問題表現，驅動深度研究的模型以 26.6%的準確率創下新高。該測試包含超過 3,000 道涵蓋 100 多個學科的多選題與簡答題，從語言學到火箭科學，從古典學到生態學。與 OpenAI o1 相比，模型在化學、人文社會科學和數學方面的提升最為顯著。驅動深度研究的模型展現了類似人類的方式，能在必要時有效尋求專業資訊。

ModelAccuracy (%)  準確率 (%)
GPT-4o3.3
Grok-23.8
Claude 3.5 Sonnet4.3
Gemini Thinking6.2
OpenAI o19.1
DeepSeek-R1*9.4
OpenAI o3-mini (medium)*10.5
OpenAI o3-mini (high)*13.0
OpenAI deep research**  OpenAI 深度研究**26.6
* Model is not multi-modal, evaluated on text-only subset.
* 模型非多模態，僅在文本子集上進行評估。
**with browsing + python tools
**包含瀏覽 + Python 工具

GAIA

On GAIA⁠(opens in a new window)1, a public benchmark that evaluates AI on real-world questions, the model powering deep research reaches a new state of the art (SOTA), topping the external leaderboard⁠(opens in a new window). Encompassing questions across three levels of difficulty, successful completion of these tasks requires abilities including reasoning, multi-modal fluency, web browsing, and tool-use proficiency.
在 GAIA⁠ 1 ——一項評估 AI 在真實世界問題表現的公開基準中，驅動深度研究的模型達到新的技術巔峰（SOTA），並登上外部排行榜首位。該基準涵蓋三個難度級別的問題，成功完成這些任務需要包括推理、多模態流暢性、網頁瀏覽及工具使用能力等技能。

GAIA
Level 1  級別 1Level 2  級別 2Level 3  級別 3Avg.  平均
Previous SOTA  先前的 SOTA(opens in a new window)67.9267.4442.3163.64
Deep Research (pass@1)  深度研究 (pass@1)74.2969.0647.667.36
Deep Research (cons@64)  深度研究 (cons@64)78.6673.2158.0372.57

GAIA task examples  GAIA 任務範例

In Nature journal's Scientific Reports conference proceedings from 2012, in the article that did not mention plasmons or plasmonics, what nano-compound is studied? Don't use the prefix nano in your answer if there is one.
在《自然》期刊的 Scientific Reports 2012 年會議論文中，哪一篇未提及等離子體或等離子體光學的文章研究了什麼奈米化合物？若答案中有奈米前綴，請勿使用。

[The remaining Chain-of-Thought messages and the final answer are hidden to protect the benchmark's ground truth answer.]
[為保護基準答案的準確性，隱藏其餘的思路鏈訊息和最終答案。]

Clarifying the search  搜尋澄清

I’m piecing together the request for an article from 2012 in Scientific Reports that details a nano-compound, excluding any related to plasmonics. Likely, this involves filtering specific topics or compounds, like silver or graphene.
我正在拼湊有關 2012 年 Scientific Reports 期刊中一篇關於奈米化合物的文章的請求，排除任何與等離子體光學相關的內容。可能需要篩選特定主題或化合物，比如銀或石墨烯。

Piecing together details  拼湊細節

I'm sub-searching "Scientific Reports 2012 nanoparticle," likely seeking nano-compounds. Interested in filtering out plasmon-related topics, possibly focusing on silver, graphene, or quantum dots.
我正在搜尋“Scientific Reports 2012 奈米粒子”，可能是在尋找奈米化合物。感興趣的是排除與等離子體相關的主題，可能專注於銀、石墨烯或量子點。

[search action]  [搜尋行動]

Navigating search filters
使用搜尋篩選器

I’m digging into advanced search filters on nature.com and Scientific Reports for 2012, trying to pinpoint articles on amorphous carbon nanoparticles in food caramels.
我正在深入 nature.com 和 Scientific Reports 的高級搜尋篩選，尋找 2012 年的文章，試圖鎖定有關食品焦糖中的非晶態碳奈米粒子的研究。

Identifying conference proceedings
辨識會議論文集

The search results reveal a conference proceeding titled "2D quasiperiodic plasmonic crystals" from 2012, likely found under the "Conference Proceedings" category in Scientific Reports.
搜尋結果顯示了一篇標題為「2D 準週期性等離子晶體」的 2012 年會議論文，可能在 Scientific Reports 的「會議論文集」類別下找到。

[browse action]  [瀏覽動作]

Reflecting on potential issues
反思潛在問題

I'm considering the reasons behind a web page misconception, likely due to an outdated agent type or inadvertent blockage.
我正在考慮導致網頁誤解的原因，可能是因為代理類型過時或意外阻擋。

[browse action]  [瀏覽動作]

Considering dynamic options
考慮動態選項

I'm thinking about a nature site with dynamic content or a cached version. Alternatively, trying a search for specific phrases might help.
我正在思考一個具有動態內容或快取版本的自然網站。或者，嘗試搜尋特定語句可能會有所幫助。

[search action]  [搜尋行動]

Pinpointing key features  確定關鍵特徵

OK, I'm identifying four conference proceedings from 2012 in Sci Rep. Notably, some mention "plasmonic" and should be excluded.
好的，我正在辨識 Sci Rep 中 2012 年的四篇會議論文。值得注意的是，有些提到「等離子」應該被排除。

[browse action]  [瀏覽動作]

Piecing together insights
拼湊見解

I’m gathering info on accessing cached versions or snippet expansions for specific titles to ensure comprehensive analysis.
我正在收集有關訪問快取版本或擴展特定標題片段的資訊，以確保分析全面。

[search action]  [搜尋行動]

Pulling together the details
整合細節

I’m noting the progression from the 2012 European Materials Research Society Spring Meeting to the scientific report, focusing on conference proceedings. This approach helps establish context and relevance.
我正在記錄從 2012 年歐洲材料研究學會春季會議到科學報告的進展，專注於會議論文集。這種方法有助於建立背景和相關性。

[search action]  [搜尋行動]

Tracking journal clues  追蹤期刊線索

Trying to connect conference proceedings from late 2012 with "Scientific Reports" journal, focusing on plasmonic 2D quasiperiodic materials. This approach feels methodical and precise.
嘗試將 2012 年底的會議論文集與「Scientific Reports」期刊連結，專注於等離子 2D 準週期性材料。這種方法感覺既有條理又精確。

[search action]  [搜尋行動]

Analyzing event details  分析事件細節

Noting that the search results [8] include a page likely summarizing the European Materials Research Society 2012 Spring Meeting, including 25 symposia.
注意到搜尋結果 [8] 包含一個頁面，可能總結了 2012 年歐洲材料研究學會春季會議的內容，其中包括 25 個研討會。

[browse action]  [瀏覽動作]

Piecing together research
整合研究

I’m examining a likely collection page with 4 articles, considering plasmonic and metamaterial topics, and identifying key references from the European Materials Research Society 2012 Spring Meeting.
我正在查看一個可能包含 4 篇文章的集合頁面，考慮到等離子體和超材料主題，並識別出 2012 年歐洲材料研究學會春季會議的關鍵參考資料。

[browse action]  [瀏覽動作]

Pinpointing the focus  確定重點

I’m focusing on the 2012 conference proceedings in "Scientific Reports" from E-MRS, likely involving topics like "2D quasiperiodic plasmonic crystals" and "Layered plasmonic cloaks to tailor the optical scattering at the nanoscale."
我正專注於歐洲材料研究學會 2012 年會議的《Scientific Reports》會議論文集，可能涉及主題如「二維準週期等離子晶體」和「分層等離子隱形衣以調整納米尺度的光學散射」。

[browse action]  [瀏覽動作]

Evaluating library sources
評估圖書館資源

Thinking about special issue E-MRS 2012 Sci rep invited paper Monticone and metamaterials lab at News Archives – 2012.
思考 2012 年歐洲材料研究學會《Scientific Reports》特刊的邀請論文蒙蒂科尼與超材料實驗室的相關內容，並查閱 2012 年的新聞檔案。

[browse action]  [瀏覽動作]

Assessing accessibility  評估可訪問性

I'm gathering info on potential site access issues, considering outdated status or blockages. Also, evaluating the Monticone site for a list reference.
我正在收集有關可能的網站訪問問題的資訊，考慮到過時狀態或封鎖情況。同時，評估蒙蒂科尼網站是否有參考清單。

[browse action]  [瀏覽動作]

Piecing together E-MRS 2012
整合歐洲材料研究學會 2012 年內容

I'm trying to link "Layered Plasmonic Cloaks" to E-MRS 2012. Considering other possible topics like graphene and catalysis.
我正在嘗試將「分層等離子隱形衣」與 2012 年歐洲材料研究學會連結起來，並考慮其他可能的主題，例如石墨烯和催化。

[browse action]  [瀏覽動作]

Identifying relevant topics
確定相關主題

I’m gathering info on topics like graphene, photovoltaics, and nanostructured compounds, while excluding plasmonic-related ones. This leads me to focus on nanotechnology aspects more broadly.
我正在收集有關石墨烯、光伏技術和納米結構化化合物等主題的資訊，同時排除與等離子相關的主題。這使我更專注於納米技術的廣泛層面。

[browse action]  [瀏覽動作]

Adjusting my focus  調整我的重點

I’m broadening the search parameters by entering specific site and scholar identifiers to pinpoint articles about 2D quasiperiodic plasmonic crystals in Nature Scientific Reports from 2012.
我正在透過輸入特定的網站和學者標識符來擴展搜尋參數，以定位 2012 年《自然科學報告》中關於二維準週期等離子晶體的文章。

[search action]  [搜尋行動]

Identifying non-plasmonic possibilities
確定非等離子相關的可能性

I'm narrowing down the search to identify articles from the 2012 E-MRS special issue in Scientific Reports. This involves checking titles for non-plasmonic topics, like graphene or functional oxides. Progress is evident through focused searches and exclusion of plasmonic articles.
我正在縮小搜尋範圍，以找出 2012 年歐洲材料研究學會《Scientific Reports》特刊中的文章。這包括檢查標題是否涉及非等離子相關主題，例如石墨烯或功能氧化物。透過專注搜尋並排除等離子文章，進展明顯。

[search action]  [搜尋行動]

Piecing together clues  拼湊線索

I'm analyzing interactions in a graphene superlattice, likely from a Sci Rep article. Considering titles by authors like Padooru, Yakovlev, Chen, and Alu.
我正在分析石墨烯超晶格中的交互作用，可能來自一篇科學報告的文章。考慮到像 Padooru、Yakovlev、Chen 和 Alu 這些作者的標題。

[search action]  [搜尋行動]

Piecing together the puzzle
拼湊拼圖

I’m gathering info on E-MRS 2012 Spring Meeting's Sci Rep proceedings, focusing on invited papers, without plasmonic phenomena. Types under review are graphene, catalysis, energy materials, nanomedicine, and sensors.
我正在收集有關 E-MRS 2012 春季會議科學報告會議記錄的資訊，重點是受邀論文，剔除與等離子體現象相關的部分。審查類型包括石墨烯、催化、能源材料、奈米醫學和感測器。

[search action]  [搜尋行動]

Expert-Level Tasks   專家級任務

In an internal evaluation of expert-level tasks across a range of areas, deep research was rated by domain experts to have automated multiple hours of difficult, manual investigation. 
在對多領域專家級任務進行的一次內部評估中，領域專家評價深度研究能自動完成數小時困難且繁瑣的手動調查。

Pass Rate vs Max Tool Calls
通過率與最大工具調用次數
00.050.10.150.2Pass Rate020406080100Max Tool Calls
The more the model browses and thinks about what its browsing, the better it does, which is why giving it time to think is important.
模型瀏覽並思考其瀏覽內容的時間越多，效果越好，因此給它時間思考非常重要。
Expert-level task examples
專家級任務範例
Time saved on task: 4 hours
節省任務時間：4 小時

I am a researcher interested in bringing mixed-gas sorption capabilities to my lab. Please discuss the differences between pure- and mixed-gas sorption for glassy polymers, how the dual-mode sorption model can be used to predict mixed-gas sorption behavior in glassy polymers (include equations where applicable), and what challenges there are in accurately predicting pure- and mixed-gas sorption using the dual-mode sorption model. Only include open-source information as of October 1, 2024.
我是一名研究員，對於在我的實驗室中引入混合氣體吸附功能感興趣。請討論玻璃態高分子中的純氣體與混合氣體吸附之差異、雙模式吸附模型如何用於預測玻璃態高分子中的混合氣體吸附行為（如適用，請包含相關方程式），以及在使用雙模式吸附模型準確預測純氣體與混合氣體吸附時面臨的挑戰。僅包含截至 2024 年 10 月 1 日的開源信息。

Pure vs. mixed-gas sorption in glassy polymers: In a glassy polymer (below TgT_g), a single gas typically exhibits a concave sorption isotherm: uptake rises sublinearly with pressure, unlike the linear Henry’s-law behavior above TgT_g. This is attributed to “dual-mode” sorption: some penetrant dissolves in the dense polymer matrix (obeying Henry’s law) while additional penetrant saturates specific microvoid “sites” (a Langmuir-type adsorption in the polymer’s nonequilibrium free volume). By contrast, when two or more gases are present, they compete for those limited Langmuir sorption sites. As a result, each component’s sorbed concentration in a mixture is generally lower than it would be at the same partial pressure alone. In particular the less soluble (lower-affinity) gas tends to be more strongly suppressed, often causing the mixed-gas solubility selectivity (ratio of concentrations) to exceed the ideal value predicted from pure-gas isotherms. For example, in CO₂/CH₄ mixtures both components sorb less than in their pure-gas cases (especially CH₄), so the CO₂/CH₄ sorption selectivity is higher under mixture conditions. In extreme cases, mixed-gas effects can even reverse performance trends observed with single gases. (We note that unlike competitive sorption, synergistic sorption – where one gas increases the sorption of another via polymer swelling – is usually minor in rigid high-free-volume polymers under moderate pressures.)
玻璃態高分子中的純氣體與混合氣體吸附：在玻璃態高分子（低於 TgT_g ）中，單一氣體通常呈現凹形吸附等溫線：吸附量隨壓力上升呈次線性增長，與高於 TgT_g 的線性亨利定律行為不同。這可歸因於“雙模式”吸附：部分滲透物溶解於緻密的高分子基質中（遵循亨利定律），而額外的滲透物則飽和於特定的微孔“位點”（在高分子非平衡自由體積中進行類 Langmuir 型吸附）。相比之下，當存在兩種或更多氣體時，它們會競爭這些有限的 Langmuir 吸附位點。因此，混合物中每種成分的吸附濃度通常低於在相同分壓下單獨存在時的濃度。特別是溶解性較差（親和性較低）的氣體受到的抑制更為顯著，這通常導致混合氣體的溶解選擇性（濃度比）超過由純氣體等溫線預測的理想值。例如，在 CO₂/CH₄混合物中，兩種成分的吸附量均低於其純氣體情況（尤其是 CH₄），因此在混合物條件下 CO₂/CH₄的吸附選擇性更高。在極端情況下，混合氣體效應甚至可能逆轉單一氣體觀察到的性能趨勢。（我們注意到，與競爭性吸附不同，協同吸附——即一種氣體通過高分子膨脹增加另一種氣體的吸附——在高剛性高自由體積高分子中於中等壓力下通常是微不足道的。）

Dual-mode sorption model and mixed-gas prediction: The dual-mode sorption (DMS) model quantitatively captures glassy polymer sorption by superposing a Henry’s-law term and a Langmuir term. For a single gas, the sorbed concentration CC (e.g. in cm³(STP) gas per cm³ polymer) is written as:
雙模式吸附模型與混合氣體預測：雙模式吸附（DMS）模型通過疊加亨利定律項與 Langmuir 項來定量描述玻璃態高分子的吸附行為。對於單一氣體，吸附濃度 CC （例如以每立方公分(STP)氣體對每立方公分高分子計算）表示為：

C(p)=kD p  +  CH′ b p 1+b p  .C(p) = k_D\,p \;+\; \frac{C'_H\,b\,p}{\,1 + b\,p\,}\,.

Here kDk_D is the Henry’s law coefficient for dissolution, CH′C'_H is the Langmuir capacity constant (proportional to the polymer’s unrelaxed free-volume or “hole” capacity), and bb is the Langmuir affinity constant. This empirical formula yields the characteristic concave isotherm of glassy polymers and reduces to simple Henry’s law at the glass-transition (since CH′→0C'_H \to 0 as T→TgT \to T_g). The same three parameters (kD,CH′,bk_D, C'_H, b) are obtained by fitting pure-gas sorption data and are then used to predict mixture sorption. For a binary mixture of gases A and B (with partial pressures pA,pBp_A, p_B), the DMS model assumes each gas obeys its own Henry’s term and that both compete for the same Langmuir sites. One convenient form is to write the total sorbed concentration as the sum of contributions from each gas:
其中 kDk_D 為溶解的亨利定律係數， CH′C'_H 為 Langmuir 容量常數（與高分子的未鬆弛自由體積或“孔洞”容量成正比）， bb 為 Langmuir 親和性常數。這個經驗公式產生了玻璃態高分子的特徵凹形等溫線，並在玻璃化轉變時簡化為單純的亨利定律（因為 CH′→0C'_H \to 0 隨 T→TgT \to T_g ）。通過擬合純氣體吸附數據獲得相同的三個參數（ kD,CH′,bk_D, C'_H, b ），然後用於預測混合物吸附。對於氣體 A 與氣體 B 的二元混合物（分壓為 pA,pBp_A, p_B ），DMS 模型假設每種氣體遵循其自身的亨利項，且兩者競爭相同的 Langmuir 位點。一種方便的形式是將總吸附濃度表示為每種氣體貢獻的總和：

Ctot=kD,ApA+kD,BpB  +   CH,A′ bA pA  +  CH,B′ bB pB  1+bApA+bBpB  ,C_{\text{tot}} = k_{D,A}p_A + k_{D,B}p_B \;+\; \frac{\,C'_{H,A}\,b_A\,p_A \;+\; C'_{H,B}\,b_B\,p_B\,}{\,1 + b_A p_A + b_B p_B\,}\,,

which implicitly means the concentration of component i in the mixture is Ci=kD,ipi+CH,i′bipi 1+∑jbjpj C_i = k_{D,i} p_i + \frac{C'_{H,i} b_i p_i}{\,1+\sum_j b_j p_j\,}. This reduces to the single-gas formula when the other component’s pressure is zero. In practice, partial pressure in these equations is often replaced by fugacity for accuracy at high pressures, though using fugacities or pressures gives nearly the same results if applied consistently. The DMS mixed-gas model predicts the competitive sorption effect observed experimentally – namely, each gas’s uptake is depressed relative to its pure-gas sorption isotherm.
這隱含了混合物中成分 i 的濃度為 Ci=kD,ipi+CH,i′bipi 1+∑jbjpj C_i = k_{D,i} p_i + \frac{C'_{H,i} b_i p_i}{\,1+\sum_j b_j p_j\,} 。當其他成分的壓力為零時，這會簡化為單一氣體的公式。在實際應用中，這些方程中的分壓通常被替換為逸度，以在高壓下提高準確性，儘管如果一致地應用逸度或壓力，兩者的結果幾乎相同。DMS 混合氣體模型預測了實驗上觀察到的競爭吸附效應——即每種氣體的吸附量相較於其純氣體的吸附等溫線有所降低。

Challenges and limitations: Although the dual-mode model is widely successful in correlating sorption data, it has notable limitations in predicting sorption behavior. First, the DMS model is empirical – its fitted parameters are not universal constants but can vary with polymer formation history, physical aging, and temperature/pressure range. This means a parameter set obtained under one set of conditions may lack predictive power outside its calibration range. For instance, sorption isotherms measured only over a limited pressure range can be fit well by DMS, but extrapolating beyond that (especially approaching plasticization pressures) may be unreliable. Indeed, multiple distinct combinations of kDk_D, CH′C'_H, bb can sometimes fit the same pure-gas isotherm almost equally well – yet they yield markedly different mixed-gas predictions. This sensitivity undermines confidence in mixture forecasts, as one study showed that using different pure-gas fit sets led to only qualitative agreement with some mixed-CO₂/CH₄ data.
挑戰與限制：雖然雙模式模型在相關吸附數據方面廣泛成功，但在預測吸附行為上有顯著限制。首先，DMS 模型是經驗性的——其擬合參數並非普適常數，而是可能隨著聚合物形成歷史、物理老化以及溫度/壓力範圍而變化。這意味著在一組條件下獲得的參數集可能在其校準範圍之外缺乏預測能力。例如，僅在有限壓力範圍內測量的吸附等溫線可以被 DMS 良好擬合，但超出該範圍（尤其接近塑化壓力）進行外推可能是不可靠的。事實上，多個不同的 kDk_D 、 CH′C'_H 、 bb 組合有時可以同樣良好地擬合相同的純氣體吸附等溫線——但它們對混合氣體的預測卻有顯著差異。這種敏感性削弱了對混合物預測的信心，因為有研究顯示使用不同的純氣體擬合參數集僅能與某些混合 CO₂/CH₄ 數據達成定性一致。

Furthermore, DMS inherently assumes a fixed population of Langmuir sites (microvoids) and ignores polymer relaxation or swelling induced by sorbate. In reality, penetrant-induced swelling (plasticization) can increase free volume and sorption capacity, especially for highly condensable gases at high activity – an effect the basic DMS model cannot capture. In binary sorption, this means any synergistic increase in sorption due to a second gas (or due to polymer dilation) is unaccounted for in the standard model. Conversely, the model also cannot describe sorption isotherm shapes that deviate from concave Langmuir-type behavior (e.g. sigmoidal isotherms caused by specific polymer-penetrant interactions or clustering). For example, glassy polymers absorbing polar molecules like alcohols can show sigmoidal sorption isotherms, which the simple dual-mode framework fails to represent. Finally, accurately measuring mixed-gas sorption itself is challenging, and discrepancies between idealized model assumptions (e.g. truly zero downstream pressure, no competitive diffusion limitations) and experimental realities can add to prediction error. Despite these challenges, the dual-mode sorption model – with appropriate care – remains a valuable first approximation for interpreting sorption in glassy polymers, and it highlights the key role of competitive site occupancy in mixed-gas environments. Researchers often turn to more elaborate models (e.g. non-equilibrium lattice fluid or thermodynamic theories) when the dual-mode model’s assumptions break down, but such models come at the cost of additional parameters or complexity. In summary, dual-mode sorption provides a useful baseline for pure- and mixed-gas sorption in glassy polymers, but one must be mindful of its empirical nature and the above limitations when predicting multicomponent sorption behavior in practice.
此外，DMS 模型本質上假設 Langmuir 位點（微孔）數量固定，並忽略了吸附物引起的聚合物鬆弛或膨脹。在現實中，滲透物誘導的膨脹（塑化作用）可能會增加自由體積和吸附容量，特別是在高活性下對高凝聚性的氣體而言——這是基本的 DMS 模型無法捕捉的效應。在二元吸附中，這意味著由於第二種氣體（或由於聚合物膨脹）導致的任何協同吸附增加，在標準模型中都未被考慮。相反，該模型也無法描述偏離凹形 Langmuir 型行為的吸附等溫線形狀（例如，由於特定聚合物-滲透物相互作用或聚集效應引起的 S 型等溫線）。例如，吸收極性分子如酒精的玻璃態聚合物可能出現 S 型吸附等溫線，而簡單的雙模式框架無法表示。最後，準確測量混合氣體吸附本身具有挑戰性，理想化模型假設（例如真正的零下游壓力、無競爭性擴散限制）與實驗現實之間的差異可能增加預測誤差。儘管存在這些挑戰，雙模式吸附模型——在適當的謹慎下——仍然是解釋玻璃態聚合物吸附的一個有價值的第一近似，並突出了混合氣體環境中競爭性位點佔據的關鍵作用。當雙模式模型的假設無法成立時，研究人員通常會轉向更複雜的模型（例如非平衡晶格流體或熱力學理論），但這些模型的代價是增加了參數或複雜性。總之，雙模式吸附為玻璃態聚合物中的純氣體和混合氣體吸附提供了一個有用的基準，但在實際預測多組分吸附行為時，必須注意其經驗性質及上述限制。

Sources: Dual-mode sorption model and mixed-gas extension; competitive sorption effects in mixtures; model equations and parameters; limitations of DMS model; parameter sensitivity and predictivity issues.
資料來源：雙模式吸附模型及混合氣體擴展；混合物中的競爭吸附效應；模型方程及參數；DMS 模型的限制；參數敏感性及預測性問題。

Understanding sorption models
了解吸附模型

I’m gathering info on pure and mixed-gas sorption in glassy polymers, focusing on the dual-mode sorption model and addressing challenges in modeling predictions.
我正在蒐集關於玻璃態高分子中純氣體與混合氣體吸附的資訊，重點放在雙模式吸附模型，並探討在模型預測中面臨的挑戰。

[search action]  [搜尋行動]

Assessing open access  評估開放取用

OK, let me see. The result from MDPI on mixed-gas sorption in glassy polymers is probably open access.
好的，讓我看看。來自 MDPI 關於玻璃態高分子中混合氣體吸附的結果可能是開放取用的。

[browse action]  [瀏覽動作]

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Examining sections  檢視章節
I’m pulling together insights from the Dual Mode Sorption Model and its extended analysis within the MDPI paper.
我正在整理來自雙模式吸附模型及其在 MDPI 論文中擴展分析的見解。

[browse action]  [瀏覽動作]

Clarifying key properties
釐清關鍵特性

I’m examining the lab report to understand Henry's law constant, Langmuir affinity constant, and Langmuir capacity constant, which describe gas sorption.
我正在檢視實驗室報告，以了解亨利定律常數、朗繆爾親和常數以及朗繆爾容量常數，這些常數描述了氣體吸附的特性。

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Piecing together  拼湊線索

I'm working through the possibility that "Equation (3)" and "Equation (4)" might be images or formula displays and considering consulting the PDF or searching for earlier references to understand them better.
我正在考慮“公式(3)”和“公式(4)”可能是圖片或公式顯示，並考慮查閱 PDF 或搜尋早期引用資料以更好地理解它們。

[browse action]  [瀏覽動作]

Confirming title relevance
確認標題相關性

I’m verifying if the document from Freeman and Koros (1980) specifically matches the reference "Model for sorption of mixed gases in glassy polymers" by checking its abstract and title.
我正在核實 Freeman 和 Koros（1980 年）的文件是否與參考文獻“混合氣體在玻璃態聚合物中吸附的模型”具體吻合，方法是檢查其摘要和標題。

[search action]  [搜尋行動]

Examining article connections
分析文章關聯

I noticed that the result from "pmc.ncbi.nlm.nih.gov" is likely the same MDPI article, and I'm curious to see if it presents the text or a simplified version.
我注意到“pmc.ncbi.nlm.nih.gov”的結果很可能是相同的 MDPI 文章，我好奇是否呈現了文本或簡化版本。

[browse action]  [瀏覽動作]

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Gathering info  收集資訊

I'm gathering information about pure and mixed gas sorption in glassy polymers. This is a critical step in understanding the material's behavior under different gas environments.
我正在收集有關純氣體和混合氣體在玻璃態聚合物中吸附的資訊。這是了解材料在不同氣體環境下行為的重要步驟。

[search action]  [搜尋行動]

Comparing gas sorption  比較氣體吸附

I noticed that carbon dioxide's presence in a mixture affects methane sorption in polymer, reducing methane's sorption compared to a pure gas scenario.
我注意到二氧化碳在混合氣體中的存在會影響甲烷在聚合物中的吸附，導致甲烷的吸附量比純氣體情況下減少。

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Linking concepts  連結概念

I’m thinking through how the term "extended dual mode equation mixture" might relate to dual mode superconducting cable equations. This shows potential for connecting concepts across fields.
我正在思考「延展雙模式方程混合物」這個術語可能如何與雙模式超導電纜方程相關聯。這顯示出跨領域連結概念的潛力。

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Requesting review details
請求審查詳情

I'm noting the request to review "Theories of Sorption and Transport in Polymer Membrane."
我正在記錄審查「聚合物膜中吸附與傳輸理論」的請求。

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Checking PDF link  檢查 PDF 連結

I’m curious about Result [2] and exploring the snippet, which involves extending a dual-mode sorption model to a binary mixture and expressing concentration of A in terms of partial pressures and constants.
我對結果 [2] 感到好奇，並正在探索其中的摘要，這涉及將雙模式吸附模型擴展至二元混合物，並以部分壓力和常數來表達 A 的濃度。

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Predicting sorption behavior
預測吸附行為

The dual-mode model anticipates that adding a second gas reduces sorption at a constant partial pressure, unlike pure gas scenarios.
雙模式模型預測，在恆定的部分壓力下，添加第二種氣體會減少吸附，這與純氣體情況不同。

[browse action]  [瀏覽動作]

Fetching a PDF  正在獲取 PDF

I'm gathering info on a nature.com PDF, possibly an older Yampol'ski article, but thinking about alternative sources like the University of Texas at Austin repository for a text snippet.
我正在收集 nature.com 上一份 PDF 的資訊，可能是 Yampol'ski 的舊文章，但也在考慮其他來源，例如德州大學奧斯汀分校的資料庫，用於獲取文本片段。

[browse action]  [瀏覽動作]

Evaluating repository options
正在評估資料庫選項

I’ve discovered that the UT repository result might not display correctly. Searching within it is uncertain. Alternatively, result [3] contains a snippet describing the dual mode mixture sorption model, explaining competitive effects and its adequacy in modeling experimental data.
我發現德州大學資料庫的結果可能無法正確顯示，在其中搜尋也不確定是否可行。另一個選擇是結果 [3]，其中包含一段描述雙模式混合吸附模型的片段，解釋了競爭效應及其在模擬實驗數據方面的適用性。

[search action]  [搜尋行動]

Refining the focus  正在聚焦重點

I'm adjusting my approach to ensure the search targets the right context.
我正在調整我的方法，以確保搜尋目標正確的上下文。

[search action]  [搜尋行動]

Understanding sorption models
了解吸附模型

I’m detailing the dual-mode sorption model for pure gases: Henry dissolving and Langmuir microvoid filling. For mixed gases, competitive sorption and non-idealities are emphasized.
我正在詳細說明純氣體的雙模式吸附模型：亨利溶解和朗繆爾微孔填充。對於混合氣體，強調競爭性吸附和非理想性。

[search action]  [搜尋行動]

Exploring hero.epa.gov  探索 hero.epa.gov

Thinking through hero.epa.gov's potential abstract or content.
思考 hero.epa.gov 的潛在摘要或內容。

[browse action]  [瀏覽動作]

Refining the model  精煉模型

I'm gathering insights on pure and mixed gas sorption, considering factors like competition, plasticization, and parameter stability. Incorporating mixed-gas data for more accurate predictions.
我正在收集有關純氣體和混合氣體吸附的見解，考慮競爭、塑化和參數穩定性等因素。加入混合氣體數據以提高預測準確性。

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Addressing dual sorption model challenges
解決雙吸附模型的挑戰

Parameter coupling and non-uniqueness impact predictions. Absence of polymer condition changes might lead to oversights.
參數耦合和非唯一性影響預測結果。若忽略聚合物條件變化，可能導致疏漏。

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Quantifying deviation  量化偏差

I’m quantifying nonideal CO2-CH4 solubility in 6FDA-mPDA, showing mixed gas effects on solubility. Competitive sorption isn’t the sole cause of permeability selectivity decline.
我正在量化 6FDA-mPDA 中 CO2-CH4 的非理想溶解度，展示混合氣體對溶解度的影響。競爭性吸附並非選擇性滲透性下降的唯一原因。

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Incorporating references  納入參考文獻

I'm gathering citations, focusing on open-source materials like MDPI 2019 and Vopicka 2014, to support the discussion on differences and challenges in the dual-mode model for gas sorption in polymeric membranes.
我正在收集引用資料，專注於開放資源如 MDPI 2019 和 Vopicka 2014，以支持關於聚合物膜中氣體吸附雙模式模型的差異和挑戰的討論。

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Proposing sections  提出章節構想

I'm organizing sections on pure vs mixed-gas sorption, exploring the dual-mode sorption model, and detailing challenges like parameter uncertainty and mixed gas prediction.
我正在整理有關純氣體與混合氣體吸附的部分，探討雙模式吸附模型，並詳細說明如參數不確定性和混合氣體預測等挑戰。

Organizing content  整理內容

I’m focusing on the dual-mode sorption model, detailing pure and mixed-gas sorption behaviors, and highlighting key differences and challenges with bullet points and structured sections for clarity.
我專注於雙模式吸附模型，詳細描述純氣體與混合氣體的吸附行為，並以條列式和結構化的章節突顯關鍵差異與挑戰。

[search action]  [搜尋行動]

Choosing citations  選擇引用文獻

OK, let me see. I’m leaning on [3], [20], [22], and [21] for their clear insights into dual-mode sorption and gas mixtures.
好的，讓我看看。我偏向使用[3]、[20]、[22]和[21]，它們對雙模式吸附和混合氣體提供了清晰的見解。

Mapping out parameters  繪製參數

I’m piecing together parameter names to avoid confusion. Sticking with (k), (C_H'), and (b) for clarity. The equations for pure gas and mixture are detailed and clearly formatted.
我正在整理參數名稱以避免混淆，為了清楚起見，使用(k)、(C_H')和(b)。純氣體和混合氣體的公式詳細且清晰地呈現出來。

Addressing challenges  解決挑戰

I'm tackling multi-solution parameter fits, polymer plasticization, and temporal effects. These intricacies make predicting pure and mixed gas sorption tricky.
我正在處理多解參數擬合、聚合物塑化以及時間效應等問題。這些複雜性使得預測純氣體和混合氣體吸附變得困難。

Piecing together  拼湊線索

I’m structuring sections on sorption differences, the dual-mode model for pure gases, its extension to mixed gases, and the associated challenges. This progression seems logical and effective.
我正在結構化整理有關吸附差異、純氣體的雙模式模型、其延伸至混合氣體的部分，以及相關挑戰的章節。這樣的進程看起來合乎邏輯且有效。

Pass Rate on Expert-Level Tasks by Estimated Economic Value
按估算的經濟價值劃分的專家級任務通過率
0510152025Pass RateLowMediumHighVery HighEstimated Economic Value
Pass Rate on Expert-Level Tasks by Estimated Hours
按估算的工作時數劃分的專家級任務通過率
0510152025Pass Rate1-3 hour(s)4-6 hours7-9 hours10+ hoursEstimated Hours
Estimated economic value of task is more correlated with pass rate than # of hours it would take a human – the things that models find difficult are different to what humans find time-consuming.
任務的估算經濟價值與通過率的相關性高於完成該任務所需的人工時數——模型覺得困難的事與人類覺得費時的事有所不同。

Limitations  限制

Deep research unlocks significant new capabilities, but it’s still early and has limitations. It can sometimes hallucinate facts in responses or make incorrect inferences, though at a notably lower rate than existing ChatGPT models, according to internal evaluations. It may struggle with distinguishing authoritative information from rumors, and currently shows weakness in confidence calibration, often failing to convey uncertainty accurately. At launch, there may be minor formatting errors in reports and citations, and tasks may take longer to kick off. We expect all these issues to quickly improve with more usage and time.
深度研究解鎖了許多新的能力，但目前仍處於早期階段，並且存在一些限制。根據內部評估，它有時可能在回應中幻化出事實或做出錯誤推論，但其錯誤率顯著低於現有的 ChatGPT 模型。它可能難以區分權威資訊和謠言，目前在信心校準方面也顯弱點，經常無法準確傳達不確定性。在發布初期，報告和引用中可能會有輕微的格式錯誤，任務啟動時間可能較長。我們預期隨著使用頻率的增加和時間的推移，所有這些問題將迅速改善。

Access  訪問權限

Deep research in ChatGPT is currently very compute intensive. The longer it takes to research a query, the more inference compute is required. We are starting with a version optimized for Pro users today, with up to 100 queries per month. Plus and Team users will get access next, followed by Enterprise. We are still working on bringing access to users in the United Kingdom, Switzerland, and the European Economic Area. 
ChatGPT 中的深度研究目前計算需求非常高。查詢所需的研究時間越長，所需的推論計算量就越多。我們今天推出了一個針對專業用戶優化的版本，每月可提供多達 100 次查詢。Plus 和 Team 用戶將是下一批獲得訪問權限的，隨後是企業用戶。我們仍在努力為英國、瑞士和歐洲經濟區的用戶提供訪問權限。

All paid users will soon get significantly higher rate limits when we release a faster, more cost-effective version of deep research powered by a smaller model that still provides high quality results. 
當我們推出一個由較小模型驅動的更快速、更具成本效益的深度研究版本時，所有付費用戶都將很快獲得顯著更高的使用限制，而該版本仍然能提供高品質的結果。

In the coming weeks and months, we’ll be working on the technical infrastructure, closely monitoring the current release, and conducting even more rigorous testing. This aligns with our principle of iterative deployment. If all safety checks continue to meet our release standards, we anticipate releasing deep research to Plus users in about a month.
在接下來的幾週和幾個月中，我們將致力於技術基礎設施的建設，密切監控當前的版本發佈，並進行更嚴格的測試。這符合我們漸進式部署的原則。如果所有安全檢查繼續符合我們的發佈標準，我們預計將在約一個月內向 Plus 用戶推出 deep research。

What's next  接下來的計畫

Deep research is available today on ChatGPT web, and will be rolled out to mobile and desktop apps within the month. Currently, deep research can access the open web and any uploaded files. In the future, you’ll be able to connect to more specialized data sources—expanding its access to subscription-based or internal resources—to make its output even more robust and personalized.
deep research 今天已經在 ChatGPT 網頁版上提供，並將於本月內推向手機和桌面應用程式。目前，deep research 可存取開放網路以及任何上傳的檔案。未來，您將能連接到更多專業化的數據來源，擴展其對訂閱制或內部資源的存取能力，讓其輸出更加強大且個人化。

Looking further ahead, we envision agentic experiences coming together in ChatGPT for asynchronous, real-world research and execution. The combination of deep research, which can perform asynchronous online investigation, and Operator, which can take real-world action, will enable ChatGPT to carry out increasingly sophisticated tasks for you.
展望未來，我們設想 ChatGPT 將結合代理體驗，進行非同步的實際研究與執行。deep research 能夠執行非同步的線上調查，而 Operator 能採取現實世界中的行動，兩者結合將讓 ChatGPT 為您完成越來越複雜的任務。

Livestream replay
Watch the replay  觀看重播

Footnotes  註腳

  1. 1

    We found that the ground-truth answers for this dataset were widely leaked online and have blocked several websites or URLs accordingly to ensure a fair evaluation of the model.
    我們發現此數據集的真實答案已廣泛洩漏至網路上，因此我們已相應封鎖了幾個網站或 URL，以確保模型評估的公平性。

Authors  作者

OpenAI

Research Leads   研究領導

Isa Fulford, Zhiqing Sun

Foundational Contributors
基礎貢獻者

Alex Tachard Passos, Alexandra Barr, Allison Tam, Charlotte Cole, Hyung Won Chung, Jason Wei, Jon Blackman, Scott Mayer McKinney, Valerie Qi

Core Contributors  核心貢獻者

Research  研究

Elaine Ya Le, Eric Mitchell, Eric Wallace, Ignasi Clavera, Leo Liu, Lorenz Kuhn, Louis Feuvrier, Max Schwarzer, Saachi Jain, Scottie Yan, Shunyu Yao, Vitchyr Pong

Deployment  部署

Carpus Chang, Harry Zhao, Joseph Trasatti, Joshua Dickens, Matt Kaufer, Mike Trpcic, Minnia Feng, Neel Ajjarapu, Peter Vidani,  Sean Fitzgerald
Carpus Chang, Harry Zhao, Joseph Trasatti, Joshua Dickens, Matt Kaufer, Mike Trpcic, Minnia Feng, Neel Ajjarapu, Peter Vidani, Sean Fitzgerald

Contributors  貢獻者

Research  研究

Ahmed El-Kishky, AJ Ostrow, Alexander Wei, Andrei Gheorghe, Andrew Kondrich, Andrey Mishchenko, Anuj Nair, Behrooz Ghorbani, Brydon Eastman, Chak Li, Foivos Tsimpourlas, Francis Song, Giambattista Parascandolo,Gildas Chabot, Haitang Hu, Hessam Bagherinezhad, Hongyu Ren, Henry Aspegren, Hunter Lightman, Ilya Kostrikov, Ilge Akkaya, James Lennon, Jean Harb, Jonathan Ward, Kai Chen, Katy Shi, Kevin Liu, Kevin Yu, Manuka Stratta, Marvin Zhang, Mengyuan Yan,  Mostafa Rohaninejad, Noam Brown, Phoebe Thacker, Raz Goan, Reah Miyara, Spencer Papay, Taylor Gordon, Wenda Zhou, Yash Patil, Yann Dubois, Yushi Wang, Wenlei Xie, Wyatt Thompson, Youlong Chen
Ahmed El-Kishky, AJ Ostrow, Alexander Wei, Andrei Gheorghe, Andrew Kondrich, Andrey Mishchenko, Anuj Nair, Behrooz Ghorbani, Brydon Eastman, Chak Li, Foivos Tsimpourlas, Francis Song, Giambattista Parascandolo, Gildas Chabot, Haitang Hu, Hessam Bagherinezhad, Hongyu Ren, Henry Aspegren, Hunter Lightman, Ilya Kostrikov, Ilge Akkaya, James Lennon, Jean Harb, Jonathan Ward, Kai Chen, Katy Shi, Kevin Liu, Kevin Yu, Manuka Stratta, Marvin Zhang, Mengyuan Yan, Mostafa Rohaninejad, Noam Brown, Phoebe Thacker, Raz Goan, Reah Miyara, Spencer Papay, Taylor Gordon, Wenda Zhou, Yash Patil, Yann Dubois, Yushi Wang, Wenlei Xie, Wyatt Thompson, Youlong Chen

+ all the contributors to o3.
+ 所有對 o3 的貢獻者。

Safety Systems  安全系統

Adam Kalai, Andrea Vallone, Elizabeth Proehl, Evan Mays, Grace Zhao, Irina Kofman, Joaquin Quinonero Candela, Lama Ahmad, Leon Maksin, Michele Wang, Miles Wang, Sam Toizer, Sandhini Agarwal, Olivia Watkins, Owen Campbell-Moore, Patrick Chao, Kristen Ying, Tejal Patwardhan, Tina Sriskandarajah, Troy Peterson, Yaodong Yu
Adam Kalai、Andrea Vallone、Elizabeth Proehl、Evan Mays、Grace Zhao、Irina Kofman、Joaquin Quinonero Candela、Lama Ahmad、Leon Maksin、Michele Wang、Miles Wang、Sam Toizer、Sandhini Agarwal、Olivia Watkins、Owen Campbell-Moore、Patrick Chao、Kristen Ying、Tejal Patwardhan、Tina Sriskandarajah、Troy Peterson、Yaodong Yu

Comms  通訊

Adam Koppel, Adam Wells, Adele Li, Andy Applebaum, Andrey Malevich, Andrew Duberstein, Andrew Howell, Anton Tananaev, Ashley Tyra, Brandon Walkin, Bryan Ashley, Cary Bassin, Cary Hudson, Cory Decareaux, Cristina Scheau, Derek Chen, David Robinson, Dibya Bhattacharjee, Drea Lopez, Eric Antonow, Eric Burke, Filippo Raso, Fotis Chantzis, Freddie Sulit, Harris Cohen, Heather Whitney, Jay Dixit, Jeffrey Han, Jen Robinson, Jessica Shieh, Joel Parish, Kan Wu, Kevin Gladstone, Kshitij Wadhwa, Leo Vandriel, Leyton Ho, Liang Chen, Madeline Christian, Mamie Rheingold, Matt Jones, Michelle Fradin, Mike McClay, Mingxuan Wang, Nacho Soto, Niko Felix, Patrick Delaney, Paul McMillan, Philip Pronin, Rodrigo Riaza Perez, Samuel Miserendino, Scott Ethersmith, Steven Baldwin, Thomas Dimson, Tomo Hiratsuka, Yaming Lin, Yara Khakbaz, Yining Chen
Adam Koppel、Adam Wells、Adele Li、Andy Applebaum、Andrey Malevich、Andrew Duberstein、Andrew Howell、Anton Tananaev、Ashley Tyra、Brandon Walkin、Bryan Ashley、Cary Bassin、Cary Hudson、Cory Decareaux、Cristina Scheau、Derek Chen、David Robinson、Dibya Bhattacharjee、Drea Lopez、Eric Antonow、Eric Burke、Filippo Raso、Fotis Chantzis、Freddie Sulit、Harris Cohen、Heather Whitney、Jay Dixit、Jeffrey Han、Jen Robinson、Jessica Shieh、Joel Parish、Kan Wu、Kevin Gladstone、Kshitij Wadhwa、Leo Vandriel、Leyton Ho、Liang Chen、Madeline Christian、Mamie Rheingold、Matt Jones、Michelle Fradin、Mike McClay、Mingxuan Wang、Nacho Soto、Niko Felix、Patrick Delaney、Paul McMillan、Philip Pronin、Rodrigo Riaza Perez、Samuel Miserendino、Scott Ethersmith、Steven Baldwin、Thomas Dimson、Tomo Hiratsuka、Yaming Lin、Yara Khakbaz、Yining Chen

Leadership  領導團隊

Akshay Nathan, Greg Brockman, Hannah Wong, Jakub Pachocki, Jerry Tworek, Johannes Heidecke, Josh Tobin, Liam Fedus, Mark Chen, Mia Glaese, Nick Turley, Sam Altman, Wojciech Zaremba
Akshay Nathan、Greg Brockman、Hannah Wong、Jakub Pachocki、Jerry Tworek、Johannes Heidecke、Josh Tobin、Liam Fedus、Mark Chen、Mia Glaese、Nick Turley、Sam Altman、Wojciech Zaremba