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OpenAI

2025年2月2日

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隆重介紹深入研究 (Deep Research)

這是一個能夠推理、整合大量網上資訊,並為你完成多步驟研究任務的智能代理。Pro 用戶即日起便可使用,稍後亦會向 Plus 與 Team 用戶開放。

正在載入...

2026 年 2 月 10 日更新:現時可以將深度研究連接至任何 MCP 或應用程式,並將網頁搜尋範圍限制於可靠網站,從而專注於經過認證、符合業界標準的來源。此外亦可以即時追蹤進度,隨時中斷並透過補充提示或添加新來源進行優化。我們已更新視覺體驗,精簡啟動、追蹤並檢閱研究的完整流程。


2025 年 7 月 17 日更新:深度研究現可透過 ChatGPT 智能代理,運用視覺瀏覽器進行更廣泛深入的研究。若要使用這些最新功能,只需在編輯器的下拉選單中選擇「智能代理模式」,然後直接輸入查詢內容。可以繼續透過工具選單中的「深度研究」選項使用原有深度研究功能。


2025 年 4 月 24 日更新:我們已大幅提升使用深度研究的頻率上限。Plus、Team、Enterprise 及 Edu 用戶現時每月可進行 25 次查詢,Pro 用戶可使用 250 次,而免費用戶則可使用 5 次。這項提升得益於一項全新輕量版深度研究功能,該版本由一個 o4-mini 版本驅動,在維持高質素功能的同時更具成本效益。達致完整版本使用次數上限後,系統將自動切換至輕量版處理查詢。


2025 年 2 月 25 日更新:所有 Plus 用戶現時可以使用深度研究。


2025 年 2 月 5 日更新:深度研究現已可供英國、瑞士及歐洲經濟區的 Pro 用戶使用。


如今,我們在 ChatGPT 中推出「深入研究」這項嶄新的智能代理功能,可在互聯網上進行多步驟研究,處理複雜任務。該功能在短短數十分鐘內,就能完成人類需花費數小時才能處理的研究工作。

深入研究是 OpenAI 新一代的自主代理工具。只需輸入提示,ChatGPT 便會自動搜尋、分析並整合數以百計的網上資訊來源,製作出全面報告,水平堪比研究分析師。此功能由即將推出、以最佳化於網頁瀏覽與資料分析的 OpenAI o3 模型版本為核心。它能透過推理能力搜尋、解析並分析互聯網上的大量文字、圖像與 PDF,並根據所遇資訊靈活調整研究路徑。

整合知識的能力,要創造新知識的先決條件。正因如此,深入研究功能是邁向通用人工智能 (AGI) 願景的重要里程碑,而我們長久以來一直希望能憑此開創出革新的科學研究成果。

為何我們開發深入研究

深入研究專門為需要從事專業知識類工作的用戶而設,涵蓋金融、科學、政策制定、工程等領域,這些用戶需要深入、精確且可信賴的研究支援。同樣地,深入研究功能對於重視資訊判斷的消費者而言也極具價值。例如在選購汽車、家電或傢俬等需要仔細研究的商品時,此功能可提供極為個人化的推薦。每一項輸出內容都附有完整的文件紀錄、清晰的引文出處與推理摘要,方便引用與驗證資訊來源。深入研究尤其擅長找出那些專門而且不直觀、需耗費大量時間、瀏覽多個網站才能獲得的資訊。透過將複雜耗時的網絡研究任務簡化為一次查詢,深入研究能為你節省寶貴時間。

深入研究能獨立地發現、推論並整合來自網絡各處的見解。為實現這一目標,深入研究採用了與 OpenAI 首款推理模型 o1 相同的強化學習方法,並接受了需要使用瀏覽器與 Python 工具的真實任務訓練。儘管 o1 已在程式設計、數學與其他技術領域展現出強大能力,許多現實世界中的挑戰,仍需從多元的網上來源蒐集大量背景資訊。深入研究正是建立在這些推理能力之上,彌補此一落差,協助人們處理在工作與日常生活中遇到的複雜問題。

如何使用深入研究

進入 ChatGPT 後,請在訊息編輯器選擇「深入研究」,並輸入查詢內容。然後,請告訴 ChatGPT 你需要甚麼資訊。你既可以要求分析串流平台的競爭情況,也可以要求為你度身推介最適合在上下班時用來代步的單車。你可以附加檔案或試算表,為問題提供更多情境資訊。研究啟動後,側邊欄會顯示目前進度摘要,以及所引用的資料來源。

深入研究需時約 5 至 30 分鐘不等,並會花時間深入互聯網探索資料。在這段期間,你可以離開或處理其他工作,研究完成後便會收到通知。最終結果會以報告形式呈現在聊天視窗中。在往後幾週內,我們亦會在報告中加入內嵌圖像、資料視覺效果及輸出其他分析內容,增進清晰度並提供更多背景資訊。

與深度研究相比,GPT‑4o 更適合涵蓋多種形式內容的即時對話。對於多方面、特定領域的查詢,回應必須深入詳盡,深入研究能夠進行全面深入的探索,並為各個論點提供引文資料。這正是快速摘要與經過充分記錄、驗證的答案之間的差異,而後者可作為工作成果使用。

GPT-4o

Help me find iOS and android adoption rates, % who want to learn another language, and change in mobile penetration, over the past 10 years, for top 10 developed and top 10 developing countries by GDP. Lay this info out in a table and separate stats into columns, and include recommendations on markets to target for a new iOS translation app from ChatGPT, focusing on markets ChatGPT is currently active in.

Compiling comprehensive data on iOS and Android adoption rates, the percentage of populations interested in learning another language, and changes in mobile penetration over the past decade for the top 10 developed and developing countries by GDP is challenging due to data limitations. However, based on available information, we can provide insights and recommendations for targeting markets with a new iOS translation app powered by ChatGPT.

Top 10 Developed Countries by GDP:

  1. United States
  2. Japan
  3. Germany
  4. United Kingdom
  5. France
  6. Italy
  7. Canada
  8. South Korea
  9. Australia
  10. Spain

Top 10 Developing Countries by GDP:

  1. China
  2. India
  3. Brazil
  4. Russia
  5. Indonesia
  6. Mexico
  7. Turkey
  8. Saudi Arabia
  9. Argentina
  10. South Africa

Insights:

  • iOS and Android Adoption Rates: Globally, Android holds a dominant market share of approximately 70.93%, while iOS accounts for about 28.32% as of 2024. In the United States, iOS leads with a 58.81% market share. Other developed countries like Japan, Canada, Australia, and the United Kingdom also show a higher preference for iOS devices. In contrast, developing countries such as India and Brazil have a higher prevalence of Android devices.
  • Interest in Language Learning: While specific percentages of populations interested in learning another language are not readily available, the global popularity of language learning apps like Duolingo, which had approximately 14.3 million downloads in July 2024, indicates a significant interest. This interest is likely higher in countries with diverse linguistic landscapes or where English is not the primary language.
  • Mobile Penetration Over the Past Decade: Mobile phone ownership has seen substantial growth globally. In the United States, smartphone ownership increased from 35% in 2011 to 91% in 2024. Developing countries have also experienced significant growth in mobile penetration, contributing to increased access to digital services.

Recommendations:

Considering the available data and focusing on markets where ChatGPT is currently active, the following developed countries are promising targets for a new iOS translation app:

  • United States: High iOS market share and significant interest in language learning.
  • United Kingdom: Substantial iOS user base and a multicultural population with diverse language needs.
  • Canada: Notable iOS adoption and a bilingual population (English and French), increasing the demand for translation services.

Among developing countries, the following are noteworthy:

  • China: Despite Android's dominance, the large population and growing interest in English and other languages present a significant opportunity.
  • India: High Android usage, but a growing middle class with increasing iOS adoption and a strong interest in language learning, especially English.
  • Brazil: Predominantly Android users, but with a sizable market for language learning apps, particularly for English and Spanish.

Focusing on these markets aligns with ChatGPT's current availability and leverages existing mobile adoption trends and language learning interests.

深入研究

Help me find iOS and android adoption rates, % who want to learn another language, and change in mobile penetration, over the past 10 years, for top 10 developed and top 10 developing countries by GDP. Lay this info out in a table and separate stats into columns, and include recommendations on markets to target for a new iOS translation app from ChatGPT, focusing on markets ChatGPT is currently active in.

Top 10 Developed Economies (by GDP)Smartphone OS Share, Language-Learning Interest, and Mobile Penetration Growth

Country

iOS Market Share

Android Market Share

Interest in New Language

(% of population)

Mobile Penetration Change (2013→2023)

United States

(USA)

~58% iOS vs 42% Android

(see left)

High:

70% regret losing foreign-language ability (indicating strong desire to improve)

56%→82%

smartphone penetration (↑26 pp)

Japan

~69% iOS vs 31% Android

(see left)

High:

Over 50% want to learn English

~30%→79%

(approx.) (↑~50 pp)

Germany

~39% iOS vs 60% Android

(see left)

High:

86% of Europeans say everyone should speak ≥1 foreign language (strong pro-language attitude)

40%→82%

(↑42 pp)

United Kingdom

(UK)

~52% iOS vs 47% Android

(see left)

Moderate-High:

~73% have some interest (only 27% “no interest” in new languages)

62%→82%

(↑20 pp)

France

~35% iOS vs 64% Android

(see left)

High:

86% (EU average) favor multilingualism; English widely seen as important

42%→83%

(↑40 pp)

Italy

~30% iOS vs 69% Android

(see left)

High:

86% (EU avg.) favor language learning; 1 in 4 regret not learning another

41%→85%

(↑44 pp)

Canada

~60% iOS vs 40% Android

(see left)

Moderate:

Many bilingual (English/French); rising interest in third languages (no recent % data)

56%→~80%

(↑ ~24 pp) (est., similar to US)

South Korea

~24% iOS vs 76% Android

(see left)

Moderate:

Strong English education focus; ~40% of teens are learning English

73%→~95%

(↑ ~22 pp) – already very high by 2013 (near saturation now)

Australia

~55% iOS vs 45% Android

(see left)

Moderate:

~70% see learning languages as valuable (English dominant but interest in Asian languages growing)

65%→~85%

(↑ ~20 pp) (est., high smartphone use)

Spain

~20% iOS vs 79% Android

(see left)

High:

88% of Spaniards learned a foreign language in school (e.g. English); strong cultural interest

55%→~85%

(↑ ~30 pp) (est., nearing Western Europe peers)

Top 10 Developing/Emerging Economies (by GDP)(ChatGPT availability considered)

Country

iOS Market Share

Android Market Share

Interest in New Language

(notable stats)

Mobile Penetration Change (2013→2023)

China

^(ChatGPT restricted)

~24% iOS vs 75% Android

(see left)

Very High:

Hundreds of millions learning English (but ChatGPT not officially accessible)

47%→68%

(↑21 pp)

India

~4% iOS vs 95% Android

(see left)

High:

English compulsory in many schools; strong economic motivation (80% link English to higher income)

~5%→46%

(↑ ~41 pp) (huge jump in smartphone adoption)

Brazil

~18% iOS vs 82% Android

(see left)

High:

Great demand for English – only ~5% speak it, but most see it as key skill

26%→67%

(↑40 pp)

Russia

^(ChatGPT limited)

~30% iOS vs 69% Android

(see left)

Moderate:

English learning common (compulsory in school); interest exists but ChatGPT availability is uncertain

36%→74%

(↑38 pp)

Mexico

~24% iOS vs 76% Android

(see left)

High:

Strong desire to learn English for US job/migration; ~50% of students study English in school (est.)

37%→62%

(↑25 pp)

Indonesia

~12% iOS vs 88% Android

(see left)

High:

English seen as gateway skill; government programs to improve English proficiency

<30%→68%

(↑ ~40 pp)

Turkey

~15% iOS vs 85% Android (est.)

(see left)

High:

English highly valued (universal in education);

80%

link English to better jobs

30%→75%

(↑45 pp)

Saudi Arabia

~24% iOS vs 76% Android

(see left)

Moderate:

English widely taught; 92% smartphone ownership (many already bilingual in English/Arabic)

73%→~95%

(↑ ~22 pp) (early adopter; near saturation now)

South Africa

~16% iOS vs 83% Android

(see left)

High:

Multilingual society; English and Afrikaans taught alongside native languages – cultural impetus to learn others

40%→~60%

(↑ ~20 pp) (est., steady growth)

Argentina

~12% iOS vs 88% Android

(see left)

High:

Strong interest in English (Argentina leads Latin America in English proficiency)

31%→~70%

(↑ ~39 pp) (est., sharp rise with smartphone adoption)

Sources: Smartphone OS shares are from StatCounter/Statista data (2023–24). Language-interest metrics come from surveys by Preply, Eurobarometer, Babbel, etc. (e.g. UK 73% interest; US 70% regret lack of languages; Japan >50% want English; Pearson global survey). Mobile penetration is measured as smartphone ownership percentage – dramatic growth is noted in developing markets (e.g. Brazil +40 points) and high saturation in developed ones (e.g. UK ~82%) over the last decade.


Recommendations: Based on the above, prime targets for a new ChatGPT‑powered iOS translation app would be countries with large iOS user bases and strong language-learning demand, where ChatGPT is accessible. Notably, Japan, the U.S., UK, Canada, and Australia emerge as top choices. These markets pair high iOS adoption (50–60%+ share) with significant enthusiasm for language learning – for example, Japan’s iPhone dominance and widespread desire to learn English make it ideal. English-speaking countries like the US, UK, Canada, and Australia, despite being monolingual societies, show substantial interest in learning other languages (e.g. Spanish or French), and all have over 80% smartphone penetration and millions of iPhone users.

Among emerging economies, Mexico and Brazil stand out as secondary targets: although Android prevails, their sheer population sizes mean tens of millions of iPhone users exist, and interest in English is very high for career and education reasons. These Latin American markets (where ChatGPT is active) could yield strong adoption for an iOS-only app, especially given the motivation to learn English. Conversely, China and Russia are less feasible – China’s OS split skews Android and ChatGPT is not officially available there, and Russia faces access and regulatory uncertainties.

In summary, focus on iOS-heavy, pro-learning markets: Japan and Western countries (US, UK, Canada, Australia, plus high-income Europe like Germany and France to a lesser degree) should be the first-tier targets. They combine robust iOS usage with favorable language-learning attitudes. A second tier includes large-population developing markets (Mexico, Brazil, perhaps Saudi Arabia for its historically high iPhone uptake) where interest in English is strong – these can be approached once the app gains traction in the primary markets. Such a strategy ensures the app launches where it has the greatest alignment of platform (iOS) presence and user demand for translation/language tools, maximizing the likelihood of adoption and growth.

深入研究以極詳盡的方式回應提示,並列列出前 10 個已開發國家和前 10 個開發中國家的數據,以便參考和比較。該功能使用這些資訊來提供詳細的市場進入建議,這些建議既有根據亦很實用。

運作方式

「深度研究」透過端到端強化學習方式,以各個領域中難以搜尋和推理的任務為基礎進行訓練。經過訓練後,該功能學會了規劃並執行多重步驟的路徑,以尋找所需的資料,並在需要時作出回溯與即時反應。該模型還能夠瀏覽用戶上傳的檔案、使用 Python 工具繪製和製作迭代圖表、在回應中嵌入所生成的圖表和來自網站的圖像,並引用其來源中的特定句子或段落。經過訓練後,該模型在多項聚焦於現實問題的公開評估中,達到前所未有的表現。

Humanity's Last Exam (人類最終考試)

人類最終考試(在新視窗中開啟)是一項近期發布的評估,用於測試 AI 處理多種學科專家級問題的表現。在該評估中,採用深入研究功能的模型準確率達 26.6%,創下歷來最佳成績。這項測試包含超過 3,000 條多項選擇題和簡答題,涵蓋語言學到火箭科學、古典文學到生態學等 100 多個學科。與 OpenAI o1 相比,最大的進展出現在化學、人文和社會科學以及數學領域。採使深入研究的模型,展現出類似人類所使用的方法,在必要時有效地尋找專業資訊。

模型準確度 (%)
GPT-4o3.3
Grok-23.8
Claude 3.5 Sonnet4.3
Gemini Thinking6.2
OpenAI o19.1
DeepSeek-R1*9.4
OpenAI o3-mini(中)*10.5
OpenAI o3-mini(高)*13.0
OpenAI 深度研究**26.6
* 模型并非多模態,評估僅以文字子集為基礎。
**使用瀏覽器 + python 工具

GAIA

GAIA(在新視窗中開啟)1為一項公開基準,用於評估 AI 處理真實世界問題時的效能。採用深入研究的模型達至全新最先進水平 (SOTA),並登上了外部排行榜(在新視窗中開啟)首位。該評估涵蓋三個難度級別的問題,成功完成這些任務需具備推理、多模態流暢度、網絡瀏覽和善用工具等能力。

GAIA
等級 1等級 2等級 3平均
先前的 SOTA(在新視窗中開啟)67.9267.4442.3163.64
深度研究 (pass@1)74.2969.0647.667.36
深度研究 (cons@64)78.6673.2158.0372.57

GAIA 任務例子

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.

[The remaining Chain-of-Thought messages and the final answer are hidden to protect the benchmark's ground truth answer.]

澄清搜尋

我正在整合一篇 2012 年《科學報告》中的文章請求,其中詳細介紹了一種奈米化合物,但不包括任何與等離子體相關的內容。這可能涉及過濾特定主題或化合物,如銀或石墨烯。

整合細節

我正在次級搜尋“科學報告 2012 奈米粒子”,可能尋找奈米化合物。有興趣過濾與等離子體相關的主題,可能會專注於銀、石墨烯或量子點。

[搜尋操作]

導航搜尋過濾器

我正在深入研究 nature.com 和 2012 年科學報告的高級搜尋過濾器,試圖找到有關食品焦糖中的無定形碳奈米顆粒的文章。

識別會議記錄

搜尋結果顯示,2012 年有一篇題為「2D 準週期等離子體晶體」的會議論文集,很可能是在《科學報告》的「會議論文集」類別下找到的。

[瀏覽操作]

反思潛在問題

我正在考慮網頁錯誤的原因,可能是由於代理類型過時或無意阻塞造成的。

[瀏覽操作]

考慮動態選項

我正在考慮一個具有動態內容或快取版本的自然網站。或者,嘗試搜尋特定短語可能會有所幫助。

[搜尋操作]

確定關鍵特徵

好的,我正在尋找 2012 年 Sci Rep 中的四篇會議論文集。值得注意的是,其中一些提到了“等離子體”,應該排除。

[瀏覽操作]

整合見解

我正在收集有關訪問特定標題的快取版本或片段擴展的信息,以確保進行全面的分析。

[搜尋操作]

總結細節

我注意到從 2012 年歐洲材料研究學會春季會議到科學報告的進展,並專注於會議記錄。這種方法有助於建立背景和相關性。

[搜尋操作]

追蹤日誌線索

試著將 2012 年底的會議論文集與《科學報告》期刊聯繫起來,並專注於等離子體二維準週期材料。這種方法讓人感覺有條理且精確。

[搜尋操作]

分析事件詳情

注意到搜尋結果[8]中有一頁可能總結了歐洲材料研究學會2012年春季會議,其中包括25場研討會。

[瀏覽操作]

整合研究

我正在檢查一個包含 4 篇文章的可能的收集頁面,考慮等離子體和超材料主題,並確定歐洲材料研究學會 2012 年春季會議的主要參考文獻。

[瀏覽操作]

辨明重點

我主要關注的是 E-MRS 2012 年“科學報告”會議論文集,其中可能涉及“二維準週期等離子體晶體”和“分層等離子體斗篷以調整奈米級光學散射”等主題。

[瀏覽操作]

評估圖書館資源

思考 E-MRS 2012 特刊 Sci rep 邀請論文 Monticone 和超材料實驗室新聞檔案 - 2012。

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評估可訪問性

我正在收集有關潛在網站訪問問題的資訊,考慮到過時的狀態或阻塞。另外,評估 Monticone 網站以取得清單參考。

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整合 E-MRS 2012

我正在嘗試將“分層等離子體斗篷”與 E-MRS 2012 聯繫起來。考慮其他可能的主題,如石墨烯和催化。

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確定相關主題

我正在收集有關石墨烯、光伏和奈米結構化合物等主題的信息,同時排除與等離子體相關的資訊。這使我更加廣泛地關注奈米技術方面。

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調整我的焦點

我透過輸入特定站點和學者標識符來擴大搜尋參數,以便精確定位 2012 年《自然科學報告中》中有關二維準週期等離子體晶體的文章。

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識別非等離子體的可能性

我正在縮小搜尋範圍,以找出《科學報告》中 2012 年 E-MRS 特刊中的文章。這涉及檢查非等離子體主題的標題,如石墨烯或功能氧化物。透過重點搜尋和排除等離子體文章,可以明顯看到進展。

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整合線索

我正在分析石墨烯超晶格中的相互作用,可能來自一篇 Sci Rep 文章。考慮 Padooru、Yakovlev、Chen 和 Alu 等作者的作品。

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整合謎題

我正在收集有關 E-MRS 2012 春季會議的 Sci Rep 會議記錄的信息,重點關注受邀論文,但不涉及等離子體現象。正在審查的類型包括石墨烯、催化、能源材料、奈米醫學和感測器。

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專家級任務

在一項涵蓋多個領域的專家級任務內部評估中,各領域專家經評估後確定,深度研究功能可以自動化原本需要數小時才能完成的艱辛人手調查工作。 

通過率與最多 Tool Call 次數
通過率最多 Tool Call 次數
模型瀏覽並且思考的內容越多,表現就越好,這就是為什麼給它時間思考很重要。
專家級任務例子
任務節省時間: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.

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.)

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:

C(p)=kDp  +  CHbp1+bp.C(p) = k_D\,p \;+\; \frac{C'_H\,b\,p}{\,1 + b\,p\,}\,.

Here kDk_D is the Henry’s law coefficient for dissolution, CHC'_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 CH0C'_H \to 0 as TTgT \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:

Ctot=kD,ApA+kD,BpB  +  CH,AbApA  +  CH,BbBpB1+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,ibipi1+jbjpjC_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.

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, CHC'_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.

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.

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.

了解吸附模型

我正在收集有關玻璃狀聚合物中純氣體和混合氣體吸附的信息,重點關注雙模吸附模型並解決建模預測中的挑戰。

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評估開放訪問

好的,讓我看看。MDPI 關於玻璃狀聚合物中混合氣體吸附的研究結果可能是開放取用的。

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檢查部分
我正在匯總 MDPI 論文中雙模式吸附模型及其擴展分析的見解。

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澄清關鍵屬性

我正在檢查實驗報告以了解描述氣體吸附的亨利定律常數、朗繆爾親和常數和朗繆爾容量常數。

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整合起來

我正在研究“方程式 (3)”和“方程式 (4)”可能是圖像或方程式顯示的可能性,並考慮查閱 PDF 或搜尋早期參考資料以更好地理解它們。

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確認標題相關性

我正在透過檢查其摘要和標題來驗證 Freeman 和 Koros (1980) 的文獻是否與參考文獻「玻璃狀聚合物中混合氣體吸附模型」具體匹配。

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檢查文章連結

我注意到“pmc.ncbi.nlm.nih.gov”的結果很可能是同一篇 MDPI 文章,我很好奇它是否呈現文本或簡化版本。

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收集資訊

我正在收集有關玻璃聚合物中純氣體和混合氣體吸附的資訊。這是了解物料在不同氣體環境下的行為的關鍵一步。

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比較氣體吸附

我注意到混合物中的二氧化碳會影響聚合物中甲烷的吸附,與純氣體相比,會降低甲烷的吸附量。

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連接概念

我正在思考“擴展雙模方程式混合”這個術語與雙模超導電纜方程式有何關係。這顯示出跨領域概念連結的巨大潛力。

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請求審核詳細信息

我注意到審查“聚合物膜中的吸附和傳輸理論”的請求。

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檢查 PDF 連結

我對結果 [2] 很感到好奇,並想探索其中的程式碼片段,它涉及將雙模式吸附模型擴展到二元混合物,並用分壓和常數來表示 A 的濃度。

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預測吸附行為

雙模式模型預計,與純氣體場景不同,添加第二種氣體會降低恆定分壓下的吸附量。

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取得 PDF

我正在收集有關 nature.com PDF 的信息,可能是一篇較舊的 Yampol'ski 文章,但正在考慮其他來源,例如University of Texas at Austin存儲庫中的文本片段。

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評估儲存庫選項

我發現 UT 存儲庫結果可能無法正確顯示。在其中搜索是不確定的。或者,結果 [3] 包含描述雙模式混合吸附模型的片段,解釋了競爭效應及其在建模實驗數據中的充分性。

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改善焦點

我正在調整我的方法以確保搜尋針對正確的上下文。

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了解吸附模型

我詳細介紹了純氣體的雙模吸附模型:Henry溶解和Langmuir微孔填充。對於混合氣體,強調競爭吸附和非理想性。

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探索 hero.epa.gov

思考 hero.epa.gov 的潛在摘要或內容。

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改善模型

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。結合混合氣體數據進行更準確的預測。

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解決雙吸附模型挑戰

參數耦合和非唯一性影響預測。缺乏聚合物狀態變化可能會導致疏忽。

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量化偏差

我正在量化 6FDA-mPDA 中的非理想 CO2-CH4 溶解度,顯示混合氣體對溶解度的影響。競爭吸附並不是滲透性選擇性下降的唯一原因。

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納入參考文獻

我正在收集引文,重點關注 MDPI 2019 和 Vopicka 2014 等開源材料,以支持有關聚合物膜中氣體吸附雙模模型的差異和挑戰的討論。

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提案章節

我正在組織純氣體吸附與混合氣體吸附的部分,探索雙模式吸附模型,並詳細說明參數不確定性和混合氣體預測等挑戰。

組織內容

我專注於雙模式吸附模型,詳細說明純氣體和混合氣體的吸附行為,並使用項目符號和結構化部分強調主要差異和挑戰,以便清晰地理解。

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選擇引用

好的,讓我看看。我依靠[3],[20],[22]和[21]對雙模吸附和氣體混合物的清晰見解。

將參數繪製出來

我正在整合參數名稱以避免混淆。為了清楚起見,請堅持使用 (k)、(C_H') 和 (b)。純氣體和混合物的方程式詳細且格式清晰。

應對挑戰

我正在解決多解決方案參數擬合、聚合物塑化和時間效應。這些複雜性使得預測純氣體和混合氣體的吸附變得非常棘手。

整合起來

我正在建立有關吸附差異、純氣體的雙模模型、其對混合氣體的擴展以及相關挑戰的部分。這種進展看起來合乎邏輯且有效。

專家級任務的通過率(按估計經濟價值)
通過率估計經濟價值
專家級任務的通過率(按估計時數)
通過率估計時數
任務的估計經濟價值與通過率的相關性,比人類所需時數的相關性更高——模型認為困難的事情與人類認為耗時的事情不同。

限制

深入研究解鎖了多項重要的新功能,但目前仍處於早期階段,並存在某些限制。根據內部評估,此功能在回應中仍有機會錯誤推論或虛構事實,但出現此類情況的機率比現有 ChatGPT 模型明顯較低。此外,此功能有時難以明確區分權威資訊與傳聞,而且在信心校準方面的表現疲弱,經常無法如實傳達不確定性。在發布時,報告和引文可能存在細微的格式錯誤,並且任務可能需要更長的時間才能啟動。我們預計,這些問題一律會隨著使用量增加而隨時間迅速改善。

使用本功能

目前在 ChatGPT 中執行深入研究,需要大量的運算資源。研究一個查詢所需的時間越長,所需的推論運算就越多。我們現推出一個專為 Pro 用戶而設的版本,每月最多可執行 100 次查詢。下一步將會是開放給 Plus 和 Team 用戶使用,然後是 Enterprise 用戶。我們仍在努力讓英國、瑞士和歐洲經濟區的用戶也能使用。 

隨著我們即將推出一款運行速度更快、成本更低、但依然維持高品質輸出的深入研究小型模型版本,所有付費用戶的速率限制很快便會有顯著提升。 

在接下來的日子裡,我們將致力於開發技術基建設施,密切監察目前的發布情況,並進行更嚴格的測試。這與我們「迭代部署」的原則一致。如果所有安全檢查持續符合我們的發布標準,我們預計將在大約一個月後,開放深入研究供 Plus 用戶使用。

接下來的下一步

今天就可以在 ChatGPT 網頁版上使用深入研究,並將在一個月內推廣到流動版和桌面版應用程式。目前,深入研究可以存取開放網絡和任何上載的檔案。隨著此功能擴大至能夠存取訂閱內容或內部資源,日後你將能夠連接到更專業的資料來源,使輸出內容更可靠,而且更貼合個人需求。

展望未來,我們預期 ChatGPT 中的自主體驗將匯聚起來,用於現實情況中非同步的研究與執行流程。透過結合可執行非同步線上調查的深入研究功能,以及可執行現實操作任務的 Operator,ChatGPT 將能夠為你執行日益複雜的任務。


2025 年 2 月 3 日附錄:我們已對用於驅動深入研究功能的 o3 早期版本進行嚴格安全測試、就緒程度評估和治理審查,判斷其風險為中度(在新視窗中開啟)。我們亦進行了額外的安全測試,以加深了解深入研究網絡瀏覽功能可能帶來的其他風險,並且已採用了新的防範措施。我們將繼續徹底測試並密切監察目前的有限發布。隨著此功能逐步擴大至涵蓋 Plus 用戶,我們將在系統卡片中,分享我們對深入研究的安全見解和保障措施。

註腳

  1. 1

    我們發現此資料集的真實答案在網上廣泛洩漏,為此封鎖了數個網站或網址,以確保能公平地評估模型表現。

作者

OpenAI

研究主管

Isa Fulford及Zhiqing Sun

基礎貢獻者

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

核心貢獻者

研究

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

部署

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

項目貢獻者

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、Hessam Bagherinezhad、Haitang Hu、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、Wenlei Xie、Yash Patil、Yann Dubois、Youlong Cheng、Yushi Wang、Wyatt Thompson

以及 o3 背後所有貢獻者。

安全系統

Adam Kalai、Alex Beutel、Andrea Vallone、Andy Applebaum、David Robinson、Elizabeth Proehl、Evan Mays、Grace Zhao、Irina Kofman、Jason Phang、Joaquin Quinonero Candela、Joel Parish、Kevin Liu、Kristen Ying、Lama Ahmad、Leon Maksin、Leyton Ho、Meghan Shah、Michele Wang、Miles Wang、Phillip Guo、Olivia Watkins、Owen Campbell-Moore、Patrick Chao、Sam Toizer、Samuel Miserendino、Sandhini Agarwal、Tejal Patwardhan、Tina Sriskandarajah、Troy Peterson、Yaodong Yu、Yunyun Wang

部署

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、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

領導層

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