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OpenAI

2025年2月2日

リリース

deep research のご紹介

推論を使用して大量のオンライン情報を統合し、複数ステップのタスクを完了するエージェントです。現在は Pro ユーザーが利用可能で、Plus ユーザーと Team ユーザーも今後ご利用いただけるようになります。

読み込んでいます...

2025年7月17日更新:ChatGPT エージェントの一部としてビジュアルブラウザーにアクセスできるようになったことで、deep research をより深く、より広くご利用いただけるようになりました。更新された機能を利用するには、コンポーザーのドロップダウンメニューで「エージェントモード」を選択し、直接クエリを入力してください。元の deep research 機能も引き続きツールメニューの「deep research」オプションから利用できます。


2025年4月24日更新:deep research をご利用いただける回数を大幅に増やします。Plus、Team、Enterprise、Edu の各ユーザーは、毎月 25 件、Pro ユーザーは 250 件、無料ユーザーは 5 件のクエリをご利用いただけるようになりました。これは、高品質を保ちながらコスト効率を向上させるように設計されたバージョンである o4-mini を採用した deep research の新しい軽量バージョンによって実現されています。完全バージョンの制限に達すると、クエリの処理は自動的に軽量バージョンに切り替えられます。


2025 年 2 月 25 日更新:すべての Plus ユーザーが、deep research をご利用いただけるようになりました。


2025 年 2 月 5 日更新:英国、スイス、欧州経済地域の Pro ユーザーは、deep research をご利用いただけるようになりました。


本日、ChatGPT で deep research を公開いたします。これは、複雑なタスクのためにインターネット上で複数ステップのリサーチを実行する、エージェント型の新機能です。人間には何時間もかかる作業を、数十分で完了します。

deep research は、独立して作業を実行できる OpenAI の次期エージェントです。プロンプトを送信すると、ChatGPT が数百のオンライン情報源を検索し、分析し、統合してリサーチ アナリスト レベルの総合的なレポートを作成します。これは、ウェブ参照とデータ分析向けに最適化された、近日公開の OpenAI o3 モデルのバージョンを採用し、推論を活用して、膨大な量のテキスト、画像、PDF をインターネット上で検索し、解釈し、分析します。さらに、検出した情報に反応し、必要に応じて方向転換します。

知識を統合する機能は、新しい知識を生み出すための前提条件です。このために、deep research は、AGI 開発という広大な目標への意義ある一歩を踏み出します。AGI は、革新的な科学的リサーチを実現できるように当社が長らく構想してきたものです。

deep research を構築する理由

deep research は、金融、科学、政策、工学などの分野で集中的な知的作業を行い、かつ徹底的で正確な信頼できるリサーチを必要とする人々向けに構築されています。これは、車、電化製品、家具などの入念なリサーチが通常必要になる購入について超個人向けのお勧めを探している買い物客の見定めにも同じくらい役立つ可能性があります。すべての出力が、明確な引用元と思考のサマリーとともに完全に文書化されるため、参照と情報の検証が簡単になります。通常なら数多くのウェブサイトを閲覧することが必要になる、ニッチで非直感的な情報を、特に効率よく見つけます。deep research を使用すると、複雑で時間がかかるウェブ リサーチについて、クエリ 1 つで負荷が軽減され、時間が短縮されるため、貴重な時間が解放されます。

deep research では、ウェブ全体から洞察を独自に検出し、推論し、統合します。これを実現するために、当社の最初のリーズニングモデルである OpenAI o1 を支えているのと同じ強化学習メソッドを使用して、ブラウザーと Python ツールの使用が必要な現実世界のタスクに基づいて学習を実施済みです。o1 は、コーディング、数学、その他の技術分野で優れた機能を示しますが、多くの現実世界の課題に対しては、多様なオンライン情報源から膨大なコンテキストや情報を収集する作業が必要になります。deep research は、その溝を埋めるために、これらの推論機能を基盤として構築されていて、人々が仕事や日常生活で直面するさまざまな問題を引き受けます。

deep research の使用方法

ChatGPT のメッセージ作成機能で「deep research」を選択し、クエリを入力します。ChatGPT に、ストリーミング プラットフォームの競合分析や通勤に最適な自転車に関する個人向けレポートなどの要望を伝えます。質問にコンテキストを追加するために、ファイルやスプレッドシートを添付できます。実行が始まると、実行したステップと使用した情報源のサマリーがサイドバーに表示されます。

deep research で作業の完了までにかかる時間には、5 分から 30 分の幅があります。ウェブで詳細を調べる時間が必要になるためです。その間、ユーザーは離れて他のタスクに取り組むことができます。リサーチが完了すると通知が届きます。最終的な出力は、チャット内のレポートとして到着します。明確さを向上させ、コンテキストを追加するために、今後数週間で、これらのレポートに埋め込み画像、データの視覚化、その他の分析出力も追加する予定です。

deep research と比べて、GPT‑4o はリアルタイムでマルチモーダルの会話に適しています。深度と詳細が重要な多面的で分野が特定された照会について、deep research には、膨大な調査を実行し、それぞれについて引用を記載する機能があり、記述が詳細な検証済みの作業成果物として使用できる回答を作成できるため、簡単なサマリーとの間に差が開きます。

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.

Deep research

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.

deep research では、プロンプトへの応答が非常にきめ細かく、上位 10 件の先進国と上位 10 件の途上国の両方について、参照と比較がしやすいように、各国固有のデータを並べて表示しています。その情報に基づいて、情報が豊富で実用的な市場参入のための詳細な推奨事項が提示されています。

仕組み

deep research は、幅広い分野の難しいウェブ参照タスクと推論タスクについて、エンドツーエンドの強化学習を使用して学習を実施済みです。この学習を通じて、必要なデータを見つけるために複数ステップの軌跡を計画して実行し、必要に応じて後戻ってリアルタイムの情報に反応することを学習しました。このモデルでは、ユーザーがアップロードしたファイルを参照すること、Python ツールを使用してグラフをプロットし反復処理すること、生成したグラフおよびウェブサイトの画像の両方を回答に埋め込むこと、情報源から特定の文や語句を引用することも可能です。この学習の結果、現実世界の問題に特化した一般評価で最高点を更新しました。

Humanity's Last Exam (人類最後の試験)

広範な主題の専門レベルの質問で AI を試す最近リリースされた評価手法の Humanity’s Last Exam(新しいウィンドウで開く) では、deep research を採用したモデルの正確度が 26.6% になり、最高点を更新しました。このテストは、3,000 件を超える複数の選択肢と、言語学からロケット工学まで、古典からエコロジーまでの 100 を超える主題にわたる短い回答の質問で構成されています。OpenAI o1 と比べて最も大きな進歩は化学、人文科学、社会科学、数学の分野に現れました。deep research を採用したモデルは、必要に応じて特定の情報を効果的に見つけ出す人間に近いアプローチを示しました。

モデル精度(%)
GPT-4o3.3
Grok-23.8
Claude 3.5 Sonnet4.3
Gemini Thinking6.2
OpenAI o19.1
DeepSeek-R1*9.4
OpenAI o3-mini-medium*10.5
OpenAI o3-mini-high*13.0
OpenAI ディープリサーチ**26.6
* モデルはマルチモーダルではなく、テキストのみのサブセットで評価
**ブラウザと Python ツールを使用

GAIA

現実世界の質問について AI を評価する公開ベンチマークである GAIA(新しいウィンドウで開く)1 では、deep research を採用したモデルが最高水準 (SOTA) を更新し、外部のリーダーボード(新しいウィンドウで開く)で首位になりました。質問は 3 つの難易度にわたるため、これらのタスクを成功させるには、推論、多面的な表現能力、ウェブ参照、熟練したツール使用などの機能が必要になります。

GAIA
レベル1レベル2レベル3平均
以前の SOTA(新しいウィンドウで開く)67.9267.4442.3163.64
ディープリサーチ(pass@1)74.2969.0647.667.36
ディープリサーチ(cons@64)78.6673.2158.0372.57

GAIA タスクの例

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]

専門レベルのタスク

さまざまな領域にわたる専門レベルのタスクの内部評価では、deep research により、数時間かかる難しい手作業の調査が自動化されたことが各分野の専門家から評価されました。 

合格率とツール呼び出しの最大数との対比
合格率ツール呼び出しの最大数
参照中の内容に関してモデルがウェブ参照と思考にかける時間が増えると、成果が向上します。思考時間を与えることが重要なのは、このためです。
専門レベルのタスク例
タスクで節約された時間:4 時間

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

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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

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Gathering info

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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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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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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.

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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

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Incorporating references

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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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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.

Addressing challenges

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Piecing together

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.

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