2. veebruar 2025

Süvauuringu tutvustus

Agent, mis kasutab arutlust, et sünteesida suures koguses veebipõhist infot ja täita sinu eest mitmeastmelisi uurimisülesandeid. Pro kasutajatele saadaval täna, järgmiseks Plus ja Team.

Proovi ChatGPT-s

Laadimine…

10. veebruari 2026 värskendus. Nüüd saad ühendada süvauuringu mis tahes MCP või rakendusega ja piirata veebiotsinguid usaldusväärsete saitidega, et saaksid keskenduda autentitud, valdkonna standarditele vastavatele allikatele. Samuti saad nüüd jälgida edenemist reaalajas ja katkestada, et täpsustada järelviipade või uute allikatega. Oleme uuendanud visuaalset kogemust, et oleks lihtsam alustada, jälgida ja üle vaadata oma uurimistööd algusest lõpuni.

17. juuli 2025 värskendus: Süvauuring võib nüüd minna veelgi rohekm süvitsi ja laiemale, kui kasutada visuaalset lehitsejat ChatGPT agendi osana. Nendele uuendatud võimalustele juurdepääsuks vali lihtsalt koostaja rippmenüüst „agendi režiim” ja sisesta oma päring otse. Algne süvauuringu funktsionaalsus jääb kättesaadavaks tööriistamenüü valiku „süvauuring” kaudu.

24. aprilli 2025. aasta värskendus: Suurendame märkimisväärselt süvauuringute kasutamise sagedust – Plus, Team, Enterprise ja Edu kasutajad saavad nüüd 25 päringut kuus, Pro kasutajad 250 ja Free kasutajad 5. See on võimalik tänu süvauuringute uuele kergemale versioonile, mida toetab o4-mini versioon, mis on loodud kulutõhusamaks, säilitades samal ajal kõrge kvaliteedi. Kui jõuate täisversiooni limiidini, lülituvad sinu päringud automaatselt kergemale versioonile.

25. veebruari 2025 värskendus: kõik Plus kasutajad saavad nüüd kasutada süvauuringut.

5. veebruari 2025. aasta värskendus: Süvauuring on nüüd saadaval Pro kasutajatele Ühendkuningriigis, Šveitsis ja Euroopa Majanduspiirkonnas.

Täna käivitame süvauuringu ChatGPT‑s, mis on uus agendivõimekus, mis viib läbi mitmeastmelist uurimistööd internetis keerukate ülesannete jaoks. See saavutab kümnete minutitega selle, milleks inimesel kuluks mitu tundi.

Süvauuring on OpenAI järgmine agent, mis suudab sinu eest iseseisvalt tööd teha – annad sellele viiba, ja ChatGPT leiab, analüüsib ning sünteesib sadu veebiallikaid, et luua põhjalik aruanne uurimisanalüütiku tasemel. See töötab tulevase OpenAI o3 mudeli versiooniga, mis on optimeeritud veebibrauseriks ja andmeanalüüsiks ning kasutab internetis tohutu hulga teksti, piltide ja PDF-failide otsimiseks, tõlgendamiseks ja analüüsimiseks arutluskäiku, reageerides vastavalt teabele, millega kokku puutub.

Teadmiste sünteesimise võime on eeltingimus uue teadmise loomiseks. Sel põhjusel tähistab süvauuring olulist sammu meie laiema eesmärgi suunas, milleks on tehisintellekti arendamine, mida oleme juba ammu ette kujutanud kui võimelist tootma uudseid teadusuuringuid.

Why we built deep research

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.

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.

How to use deep research

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.

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.

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.

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:

United States
Japan
Germany
United Kingdom
France
Italy
Canada
South Korea
Australia
Spain

Top 10 Developing Countries by GDP:

China
India
Brazil
Russia
Indonesia
Mexico
Turkey
Saudi Arabia
Argentina
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

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 responds to the prompt in a highly detailed manner, providing side-by-side country-specific data for both top 10 developed and top 10 developing countries for easy reference and comparison. It uses that information to offer detailed market-entry recommendations that are informed and usable.

Kuidas see töötab

Süvauuring koolitati otsast lõpuni kinnistava õppimise abil keeruliste sirvimis- ja mõtlemisülesannete jaoks erinevates domeenides. Selle koolituse kaudu õppis see plaanima ja teostama mitmeastmelist trajektoori, et leida vajalikke andmeid, tagasipöördudes ja vajadusel reageerides reaalajas teabele. Mudel suudab ka sirvida kasutajate laetud üles faile, koostada ja itereerida graafikuid Python tööriista abil, manustada nii koostatud graafikuid kui ka veebilehtedelt pärit pilte oma vastustesse ning viidata konkreetsetele lausetele või lõikudele oma allikatest. Selle koolituse tulemusena saavutab see uusi kõrgusi mitmetes avalikes hinnangutes, mis keskenduvad reaalsetele probleemidele.

Inimkonna viimane eksam

Hiljuti avaldatud hindamises Humanity’s Last Exam⁠(avaneb uues aknas), mis testib tehisintellekti laias valikus teemadel ekspertide tasemel küsimuste abil, saavutab süvauuringuid toetav mudel uue rekordi 26,6% täpsusega. See test koosneb enam kui 3000 valikvastusega ja lühivastusega küsimusest enam kui 100 aines, alates keeleteadusest kuni raketiteaduseni, klassikast kuni ökoloogiani. Võrreldes OpenAI o1‑ga ilmnesid suurimad edusammud keemias, humanitaar- ja sotsiaalteadustes ning matemaatikas. Süvauuringuid toetav mudel demonstreeris inimsarnast lähenemist, otsides vajadusel tõhusalt spetsialiseeritud teavet.

Mudel	Täpsus (%)
GPT-4o	3,3
Grok-2	3,8
Claude 3.5 Sonnet	4,3
Gemini Thinking	6,2
OpenAI o1	9,1
DeepSeek-R1*	9,4
OpenAI o3-mini (medium)*	10,5
OpenAI o3-mini (kõrge)*	13,0
OpenAI süvauuringud**	26,6

* Mudel ei ole multimodaalne, seda on hinnatud ainult tekstipõhise alamhulga järgi.

**koos sirvimise ja Pythoni tööriistadega

GAIA

GAIA⁠(avaneb uues aknas)¹ avalikul võrdlusalusel, mis hindab tehisintellekti reaalsetes küsimustes, saavutab süvauuringuid toetav mudel uue tipptaseme (SOTA), tõustes välise edetabeli tippu⁠(avaneb uues aknas). Küsimusi, mis hõlmavad kolme raskusastet, nõuab nende ülesannete edukas täitmine oskusi nagu arutlemine, mitmemodaalne sujuvus, veebisirvimine ja tööriistade kasutamise oskus.

GAIA
	1. tase	Tase 2	3. tase	Keskmine
Eelmine SOTA(avaneb uues aknas)	67,92	67,44	42,31	63,64
Deep Research (pass@1)	74,29	69,06	47,6	67,36
Deep Research (cons@64)	78,66	73,21	58,03	72,57

GAIA ülesannete näited

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.

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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.

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Reflecting on potential issues

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

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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.

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

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Piecing together insights

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

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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.

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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.

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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.

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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.

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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."

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Evaluating library sources

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

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Eksperditaseme ülesanded

Ekspertide tasandi ülesannete sisehindamisel erinevates valdkondades hindasid domeenieksperdid süvauuringut selliseks, mis automatiseeris mitu tundi keerulist, käsitsi uurimist.

Läbimise määr vs maksimaalsed tööriistakutsed

Mida rohkem mudel sirvib ja mõtleb selle üle, mida ta sirvib, seda paremini see toimib, mistõttu on oluline anda talle mõtlemiseks aega.

Ekspertide tasemel ülesannete näited

Ülesande täitmisel säästetud aeg: 4 tundi

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 $T_g$ ), a single gas typically exhibits a concave sorption isotherm: uptake rises sublinearly with pressure, unlike the linear Henry’s-law behavior above $T_g$ . 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 $C$ (e.g. in cm³(STP) gas per cm³ polymer) is written as:

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

Here $k_D$ is the Henry’s law coefficient for dissolution, $C'_H$ is the Langmuir capacity constant (proportional to the polymer’s unrelaxed free-volume or “hole” capacity), and $b$ 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 $C'_H \to 0$ as $T \to T_g$ ). The same three parameters ( $k_D, C'_H, b$ ) 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 $p_A, p_B$ ), 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:

$C_{\text{tot}} = k_{D,A}p_A + k_{D,B}p_B \;+\; \frac{\,C'_{H,A}\,b_A\,p_A \;+\; C'_{H,B}\,b_B\,p_B\,}{\,1 + b_A p_A + b_B p_B\,}\,,$

which implicitly means the concentration of component i in the mixture is $C_i = k_{D,i} p_i + \frac{C'_{H,i} b_i p_i}{\,1+\sum_j b_j p_j\,}$ . 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.

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 $k_D$ , $C'_H$ , $b$ 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.

Sorptsioonimudelite mõistmine

Kogun infot puhta ja segagaasi sorptsiooni kohta klaasjastes polümeerides, keskendudes kaherežiimilisele sorptsioonimudelile ja modelleerimisennustuste väljakutsete lahendamisele.

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Avatud juurdepääsu hindamine

Olgu, las ma vaatan. MDPI tulemus klaaspolümeeride segugaaside sorptsiooni kohta on tõenäoliselt avatud juurdepääsuga.

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Sektsioonide uurimine
Ma koondan teadmisi kahemoodilise sorptsioonimudeli ja selle laiendatud analüüsi kohta MDPI artiklis.

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Võtmeomaduste selgitamine

Uurin laboratooriumi aruannet, et mõista Henry seaduskonstanti, Langmuiri afiinsuskonstanti ja Langmuiri mahtuvuskonstanti, mis kirjeldavad gaasi sorptsiooni.

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Kokku panemine

Ma kaalun võimalust, et „Võrrand (3)“ ja „Võrrand (4)“ võivad olla pildid või valemikuvad ning mõtlen PDF-faili vaatamisele või varasemate viidete otsimisele, et neid paremini mõista.

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Pealkirja asjakohasuse kinnitamine

Ma kinnitan, kas Freemani ja Korose (1980) dokument vastab täpselt viitele „Model for sorption of mixed gases in glassy polymers“, kontrollides selle kokkuvõtet ja pealkirja.

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Artiklite seoste uurimine

Ma märkasin, et "pmc.ncbi.nlm.nih.gov" tulemus on tõenäoliselt sama MDPI artikkel ja olen uudishimulik, kas see esitab teksti või lihtsustatud versiooni.

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Info kogumine

Ma kogun infot puhaste ja segatud gaaside sorptsiooni kohta klaasjastes polümeerides. See on oluline samm materjali käitumise mõistmiseks erinevates gaasikeskkondades.

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Gaasi sorptsiooni võrdlemine

Ma märkasin, et süsinikdioksiidi olemasolu segus mõjutab metaani sorptsiooni polümeeris, vähendades metaani sorptsiooni võrreldes puhta gaasi olukorraga.

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Mõistete sidumine

Ma mõtlen, kuidas termin „laiendatud kahemoodilise režiimi võrrandi segu“ võiks olla seotud kahemoodilise ülijuhtiva kaabli võrranditega. See näitab potentsiaali kontseptsioonide ühendamiseks erinevate valdkondade vahel.

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Ülevaate üksikasjade küsimine

Märgin taotlust vaadata üle "Sorptsiooni ja transpordi teooriad polümeermembraanis."

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PDF-lingi kontrollimine

Mind huvitab tulemus [2] ja selle lõiguga tutvu, mis hõlmab kahe režiimi sorptsioonimudeli laiendamist binaarseks seguks ning A kontsentratsiooni väljendamist osarõhkude ja konstantide kaudu.

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Sorptsioonikäitumise ennustamine

Kahe režiimiga mudel ennustab, et teise gaasi lisamine vähendab sorptsiooni konstantsel osarõhul, erinevalt puhta gaasi stsenaariumidest.

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PDF-i hankimine

Ma kogun infot nature.com PDF-faili kohta, võib-olla vanema Yampol'ski artikli kohta, aga mõtlen ka alternatiivsetele allikatele, näiteks Texase Ülikooli Austini varamust tekstilõigu jaoks.

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Varamu valikute hindamine

Olen avastanud, et UT varamu tulemused ei pruugi õigesti kuvada. Selles otsimine on ebakindel. Teise võimalusena sisaldab tulemus [3] fragmenti, mis kirjeldab kaherežiimilist segu sorptsioonimudelit, selgitades konkurentsiefekte ja selle sobivust eksperimentaalsete andmete modelleerimisel.

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Fookuse täpsustamine

Kohandan oma lähenemisviisi, et tagada, et otsing suunatakse õigesse konteksti.

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Sorptsioonimudelite mõistmine

Kirjeldan üksikasjalikult puhaste gaaside kaherežiimilist sorptsioonimudelit: Henry lahustumine ja Langmuiri mikrovoidi täitmine. Segatud gaaside puhul rõhutatakse konkureerivat sorptsiooni ja mitteideaalsusi.

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Tutvun lehega hero.epa.gov

Mõtiskledes lehe hero.epa.gov üle võimalik kokkuvõte või sisu.

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Mudeli täiustamine

Ma kogun teadmisi puhta ja segatud gaasi sorptsiooni kohta, arvestades selliseid tegureid nagu konkurents, plastifitseerimine ja parameetrite stabiilsus. Täpsemate prognooside saamiseks segagaaside andmete lisamine.

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Kahekordse sorptsioonimudeli väljakutsete lahendamine

Parameetrite sidumine ja mitteunikaalsus mõjutavad ennustusi. Polümeeri oleku muutuste puudumine võib viia tähelepanekuteni.

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Kõrvalekalde kvantifitseerimine

Ma kvantifitseerin mitteideaalset CO2-CH4 lahustuvust 6FDA-mPDA-s, näidates segagaaside mõju lahustuvusele. Konkureeriv sorptsioon ei ole ainus põhjus, miks läbilaskvuse selektiivsus väheneb.

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Viidete lisamine

Kogun viiteid, keskendudes avatud juurdepääsuga materjalidele nagu MDPI 2019 ja Vopicka 2014, et anda tuge arutelule polümeermembraanides gaasi sorptsiooni kaherežiimilise mudeli erinevuste ja väljakutsete üle.

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Ettepanek lõikude kohta.

Ma korraldan sektsioone puhta ja segagaasi sorptsiooni kohta, tutvun kaherežiimilise sorptsioonimudeliga ja kirjeldan üksikasjalikult väljakutseid, nagu parameetrite määramatus ja segagaasi ennustamine.

Sisu korraldamine

Keskendun kaherežiimilisele sorptsioonimudelile, kirjeldades üksikasjalikult puhta ja segagaasi sorptsioonikäitumist ning tuues selguse huvides esile peamised erinevused ja väljakutsed täpploendite ja struktureeritud osade abil.

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Viidete valimine

Olgu, las ma vaatan. Ma toetun selgete arusaamade saamiseks kaherežiimilisest sorptsioonist ja gaasisegude kasutamisest allikatele [3], [20], [22] ja [21].

Parameetrite kaardistamine

Segaduse vältimiseks panen parameetrite nimed kokku. Selguse huvides jään (k), (C_H') ja (b) juurde. Puhta gaasi ja segu võrrandid on üksikasjalikud ja selgelt vormistatud.

Väljakutsete lahendamine

Ma tegelen mitme lahenduse parameetrite sobitamise, polümeeride plastilisatsiooni ja ajaliste mõjudega. Need keerukused muudavad puhta ja segatud gaaside sorptsiooni ennustamise keeruliseks.

Kokku panemine

Ma struktureerin jaotisi sorptsioonierinevuste, puhaste gaaside kaherežiimilise mudeli, selle laiendamise segagaasidele ja sellega seotud väljakutsete kohta. See areng tundub loogiline ja tõhus.

Eksperttaseme ülesannete läbimismäär hinnangulise majandusliku väärtuse alusel

Ekspertide taseme ülesannete läbimise määr hinnanguliste tundide järgi

Ülesande hinnanguline majanduslik väärtus on rohkem seotud läbimisprotsendiga kui tundide arvuga, mida inimesel kuluks – asjad, mida mudelid peavad keeruliseks, erinevad sellest, mida inimesed peavad aeganõudvaks.

Limitations

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.

Access

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.

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.

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.

Mis järgmiseks

Põhjalikud uuringud on täna saadaval ChatGPT veebis ja need tuuakse kuu jooksul mobiili- ja lauaarvutirakendustesse. Praegu saab süvauuring ligi avatud veebile ja kõigile laetud failidele. Tulevikus saad ühenduda spetsialiseeritumate andmeallikatega—laiendades ligipääsu püsitellimusel põhinevatele või sisemistele ressurssidele—muutes selle väljundi veelgi tugevamaks ja isikupärasemaks.

Tulevikku vaadates kujutame ette, et agentide kogemused koonduvad ChatGPT‑s asünkroonseks, reaalse maailma uurimiseks ja teostamiseks. Süvauuringute ja Operator'i kombinatsioon, mis suudab teha asünkroonset veebipõhist uurimist ja pärismaailmas toiminguid, võimaldab ChatGPT‑l sinu eest järjest keerukamaid ülesandeid täita.

3. veebruari 2025. aasta lisa. Viisime läbi ranged ohutustestid, valmisoleku hindamised ja juhtimisülevaated o3 varajase versiooni kohta, mis toetab süvauuringuid, tuvastades seda kui keskmist riski.⁠(avaneb uues aknas) Samuti viisime läbi täiendavad ohutustestid, et paremini mõista süvauuringu võime veebi sirvimisega seotud täiendavaid riske, ja lisasime uusi leevendusi. Jätkame praeguse piiratud väljaande põhjalikku testimist ja tähelepanelikku jälgimist. Kui laiendame juurdepääsu Plus kasutajatele, jagame oma ohutusalaseid teadmisi ja kaitsemeetmeid süvauuringu süsteemikaardis.

Footnotes

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.

Authors

OpenAI

Research Leads

Isa Fulford, Zhiqing Sun

Foundational Contributors

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

Core Contributors

Research

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

Deployment

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

Contributors

Research

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

+ all the contributors to o3.

Safety Systems

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

Deployment

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

Leadership

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