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OpenAI

2025年2月2日

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隆重介紹深度研究

能整合大量線上資訊並進行推理的代理程式,協助您完成步驟繁瑣的研究任務。現已提供 Pro 用戶使用,後續將開放 Plus 及 Team 用戶使用。

載入中…

2026 年 2 月 10 日更新:你現在可以將深度研究連接到任何 MCP 或應用程式,並將網頁搜尋限制在受信任的網站上,讓你能專注於經過驗證、符合產業標準的來源。你也可以即時追蹤進度,並可中斷以透過後續提示詞或新來源進行微調。我們已更新視覺體驗,讓你更容易從頭到尾開始、追蹤並檢視研究。


2025 年 7 月 17 日更新:深度研究功能再進化!現在可透過 ChatGPT 智慧體的視覺化瀏覽器,進行更深入、更廣泛的研究。要使用這些更新的功能,只需從撰寫工具的下拉選單中選擇「智慧體模式」,然後直接輸入查詢。原有的深度研究功能仍可透過工具選單中的「深度研究」選項使用。


2025 年 4 月 24 日更新:我們大幅提升了你使用深度研究的次數,Plus、Team、Enterprise 和 Edu 用戶每月可使用 25 次查詢,Pro 用戶可使用 250 次,Free 用戶則為 5 次。這是透過由 o4-mini 版本驅動的全新輕量版深度研究實現的,該版本旨在提升成本效益,同時保持高品質。一旦你達到完整版的使用上限,你的查詢將自動切換為輕量版。


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


2025 年 2 月 5 日更新:深度研究現已提供給英國、瑞士及歐洲經濟區的 Pro 用戶。


目前我們已在 ChatGPT 中推出「深度研究」,這是一項全新的智慧型代理功能,能在網路上執行步驟繁瑣的研究任務,處理各類複雜問題。它能在短短數十分鐘內完成原本需要人類數小時才能完成的工作。

深度研究是 OpenAI 能獨立為您執行任務的次世代智慧型代理:您輸入提示後,ChatGPT 就能搜尋、分析並整合數百筆線上資訊,生成一份具備研究分析師水準的完整報告。它由即將推出的 OpenAI o3 模型驅動,該版本專為網頁瀏覽與資料分析而最佳化,具備推理能力,能搜尋、解讀並分析網路上的龐大資料、圖片及 PDF 檔案,並可根據所獲資訊進行動態調整。

整合知識的能力,是創造新知的前提。正因如此,深度研究意味著我們已朝著開發 AGI (通用人工智慧) 的目標邁出重要的一步。我們長久以來的願景,即是打造能夠產出創新科學研究成果的系統。

我們為什麼開發深度研究

深度研究功能專為知識密集型工作者設計,涵蓋金融、科學、政策制定及工程等專業領域,因為這些領域的從業者通常需要深入、準確且可靠的研究成果;此功能對具鑑別力的消費者同樣實用,能提供高度個人化的購買建議,特別適用於汽車、家電、家具等需要深入比較的商品。每份輸出結果皆附上完整資料來源,並摘要說明推理過程,方便使用者引用與驗證資訊的正確性。深度研究在搜尋冷門且難以從常理推斷的資訊時特別有效,因為這類資訊往往需要瀏覽大量網站才能取得。只要查詢一次,深度研究便能快速完成那些原本繁瑣、費時的網路研究,大幅節省您的時間與心力。

深度研究能獨立作業,從網路各處發掘深度見解,並進行推理與整合。為了達成這個目標,我們以需要使用瀏覽器與 Python 工具的真實任務作為訓練基礎。這種方法延續自 OpenAI 首款推理模型 o1,同樣採用強化學習技術。雖然 o1 在程式設計、數學及其他技術領域展現出色能力,但實際生活中的許多挑戰則需要大量背景資訊與跨網站搜尋。深度研究以這些推理能力為基礎,進一步補足這項落差,使其能解決大家在工作及日常生活中遇到的問題。

如何使用深度研究

在 ChatGPT 的訊息欄選擇「深度研究」並輸入您的問題。告訴 ChatGPT 您的需求:不論是串流平台的競爭現況分析,或是通勤用自行車的個人化推薦報告都可以。您也可以上傳檔案或試算表,補充問題的背景資訊。當研究開始執行後,介面右側會出現側邊欄,顯示查詢過程所採取的步驟及使用的資料來源摘要。

深度研究可能需時 5 到 30 分鐘不等,視所需搜尋深度而定。在此期間,您可以暫時離開或處理其他工作;研究完成後,系統會通知您。最終成果將以報告形式呈現在對話中。未來幾週內,我們也將陸續在這些報告中加入圖片、資料視覺化圖表及其他分析內容,以提升清晰度與脈絡理解。

與深度研究相比,GPT‑4o 更適合即時的多模態對話。深度研究能針對多面向、具特定領域背景的複雜問題,進行全面深入的探索,並為每項論點提供引註資料。這正是它與一般快速摘要的關鍵差異,因為深度研究可產出具交付價值的研究成果。

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.

深度研究

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.

深度研究會根據提示提供極其詳盡的回應,列出前 10 名已開發國家與前 10 名開發中國家的國別資料對照,方便參考與比較。它會根據這些資料,提出具參考價值且實用的市場切入建議。

如何運作

深度研究透過端對端強化學習訓練而成,專為各領域高難度的瀏覽與推理任務設計。透過這樣的訓練,它學會如何規劃並執行多步驟的搜尋流程以取得所需資料,並能在必要時回溯路徑,靈活因應新資訊。該模型能瀏覽使用者上傳的檔案,利用 Python 工具繪製與調整圖表,並在回覆中嵌入自動生成的圖表與網站圖片。此外,它還能引用來源中的具體句子或段落。經過這些訓練後,它在多項針對真實世界問題的公開評測中創下新高。

人類終極測驗

在新近發布的人類終極測驗(在新視窗中開啟)中,深度研究所使用的模型創下 26.6% 的準確率新高。這項測試涵蓋 100 多門學科,包含超過 3,000 題選擇題與簡答題形式的專家級問題,範圍橫跨語言學、火箭科學、古典學與生態學,包羅萬象相較於 OpenAI o1,最明顯的進步出現在化學、人文社會科學與數學等領域。該模型展現出類似人類的研究方式,能在需要時有效尋找專業資訊。

模型準確率 (%)
GPT-4o3.3
Grok-23.8
Claude 3.5 Sonnet4.3
Gemini Thinking6.2
OpenAI o19.1
DeepSeek-R1*9.4
OpenAI o3-mini (中)*10.5
OpenAI o3-mini (高)*13.0
OpenAI 深度研究**26.6
* 該模型不支援多模態,評估時僅使用文字資料子集。
**使用瀏覽功能 + Python 工具

生成式人工智慧助理 (GAIA)

GAIA(在新視窗中開啟) [fn:1]] 這項用以評估人工智慧處理真實世界問題能力的公開基準測試中,支援深度研究的模型刷新了技術最佳紀錄 (SOTA) ,並登上外部排行榜(在新視窗中開啟)首位。該測驗涵蓋三種難度等級的問題,完成任務需具備推理、多模態流暢度、網頁瀏覽及工具使用等能力。

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

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.

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

Clarifying the search

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.

Piecing together details

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.

[search action]

Navigating search filters

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.

Identifying conference proceedings

The search results reveal a conference proceeding titled "2D quasiperiodic plasmonic crystals" from 2012, likely found under the "Conference Proceedings" category in Scientific Reports.

[browse action]

Reflecting on potential issues

I'm considering the reasons behind a web page misconception, likely due to an outdated agent type or inadvertent blockage.

[browse action]

Considering dynamic options

I'm thinking about a nature site with dynamic content or a cached version.Alternatively, trying a search for specific phrases might help.

[search action]

Pinpointing key features

OK, I'm identifying four conference proceedings from 2012 in Sci Rep. Notably, some mention "plasmonic" and should be excluded.

[browse action]

Piecing together insights

I’m gathering info on accessing cached versions or snippet expansions for specific titles to ensure comprehensive analysis.

[search action]

Pulling together the details

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.

[search action]

Tracking journal clues

Trying to connect conference proceedings from late 2012 with "Scientific Reports" journal, focusing on plasmonic 2D quasiperiodic materials.This approach feels methodical and precise.

[search action]

Analyzing event details

Noting that the search results [8] include a page likely summarizing the European Materials Research Society 2012 Spring Meeting, including 25 symposia.

[browse action]

Piecing together research

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.

[browse action]

Pinpointing the focus

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."

[browse action]

Evaluating library sources

Thinking about special issue E-MRS 2012 Sci rep invited paper Monticone and metamaterials lab at News Archives – 2012.

[browse action]

Assessing accessibility

I'm gathering info on potential site access issues, considering outdated status or blockages.Also, evaluating the Monticone site for a list reference.

[browse action]

Piecing together E-MRS 2012

I'm trying to link "Layered Plasmonic Cloaks" to E-MRS 2012.Considering other possible topics like graphene and catalysis.

[browse action]

Identifying relevant topics

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.

[browse action]

Adjusting my focus

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.

[search action]

Identifying non-plasmonic possibilities

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.

[search action]

Piecing together clues

I'm analyzing interactions in a graphene superlattice, likely from a Sci Rep article.Considering titles by authors like Padooru, Yakovlev, Chen, and Alu.

[search action]

Piecing together the puzzle

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.

[search action]

專家級任務

在一項涵蓋多個領域的內部評估中,深度研究獲得領域專家的評定,具備自動化處理數小時高難度人工研究任務的能力。 

通過率與工具呼叫數上限比較
通過率工具呼叫數上限
模型所瀏覽和思考的內容越多,表現就會越好,因此提供思考時間相當重要。
專家級任務範例
節省的任務時間: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.

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.

Understanding sorption models

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.

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Assessing open access

OK, let me see.The result from MDPI on mixed-gas sorption in glassy polymers is probably open access.

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Examining sections
I’m pulling together insights from the Dual Mode Sorption Model and its extended analysis within the MDPI paper.

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Clarifying key properties

I’m examining the lab report to understand Henry's law constant, Langmuir affinity constant, and Langmuir capacity constant, which describe gas sorption.

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Piecing together

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Confirming title relevance

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Examining article connections

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.

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Gathering info

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.

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Comparing gas sorption

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.

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Linking concepts

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I'm noting the request to review "Theories of Sorption and Transport in Polymer Membrane."

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Checking PDF link

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Predicting sorption behavior

The dual-mode model anticipates that adding a second gas reduces sorption at a constant partial pressure, unlike pure gas scenarios.

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Fetching a 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.

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Understanding sorption models

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.

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Exploring hero.epa.gov

Thinking through hero.epa.gov's potential abstract or content.

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Refining the model

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Addressing dual sorption model challenges

Parameter coupling and non-uniqueness impact predictions.Absence of polymer condition changes might lead to oversights.

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Quantifying deviation

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.

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Incorporating references

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.

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Organizing content

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Choosing citations

OK, let me see.I’m leaning on [3], [20], [22], and [21] for their clear insights into dual-mode sorption and gas mixtures.

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Addressing challenges

I'm tackling multi-solution parameter fits, polymer plasticization, and temporal effects.These intricacies make predicting pure and mixed gas sorption tricky.

Piecing together

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