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OpenAI

2. febrúar 2025

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Við kynnum ítarlega rannsókn

Fulltrúi sem notar rökhugsun til að safna saman miklu magni af upplýsingum á netinu og ljúka fjölþrepa rannsóknarverkefnum fyrir þig. Í boði fyrir Pro notendur í dag, Plus og Team verða næst í röðinni.

Prófaðu ChatGPT(opnast í nýjum glugga)
Hleður inn...

Uppfærsla 10. febrúar 2026: Nú geturðu tengt djúprannsókn við hvaða MCP eða app sem er og takmarkað vefleitir við traust vefsvæði, svo þú getir einbeitt þér að staðfestum, iðnaðarstöðluðum heimildum. Þú getur einnig fylgst með framvindu í rauntíma og gripið inn í til að fínstilla með eftirfylgnikvaðningum eða nýjum heimildum. Við höfum uppfært sjónræna upplifunina svo það er auðveldara að byrja, fylgjast með og fara yfir rannsóknina þína frá upphafi til enda.

Uppfærsla 17. júlí 2025: Ítarlegar rannsóknir geta nú orðið enn ítarlegri og víðtækari með aðgangi að sjónrænum vafra sem hluta af ChatGPT‑fulltrúa. Til að fá aðgang að þessum uppfærðu eiginleikum skaltu velja „fulltrúastilling“ úr fellilistanum í ritlinum og slá inn fyrirspurnina þína. Upprunaleg virkni ítarlegra rannsókna er enn fáanleg með valkostinum „ítarlegar rannsóknir“ í tækjavalmyndinni.

Uppfærsla 24. apríl 2025: Við erum að auka verulega hversu oft þú getur notað ítarlegar rannsóknir — Plus, Team, Enterprise og Edu notendur fá nú 25 fyrirspurnir á mánuði, Pro notendur fá 250 og Free notendur fá 5. Þetta er gert mögulegt með nýrri léttari útgáfu af ítarlegum rannsóknum sem knúin er áfram af útgáfu af o4-mini, sem er hönnuð til að vera hagkvæmari en varðveita samt gæði. Þegar þú nærð takmörkunum fyrir fullu útgáfuna munu fyrirspurnirnar þínar sjálfkrafa skipta yfir í léttari útgáfuna.

Uppfærsla 25. febrúar 2025: Allir Plus notendur geta nú notað ítarlegar rannsóknir.

Uppfærsla 5. febrúar 2025: Ítarlegar rannsóknir eru nú aðgengilegar Pro notendum í Bretlandi, Sviss og á Evrópska efnahagssvæðinu.

Í dag erum við að kynna ítarlegar rannsóknir í ChatGPT, nýja fulltrúagetu sem framkvæmir fjölþrepa rannsóknir á internetinu fyrir flókin verkefni. Hún afrekar á tugum mínútna það sem myndi taka manneskju margar klukkustundir.

Ítarlegar rannsóknir eru næsti fulltrúi OpenAI sem getur unnið sjálfstætt fyrir þig – þú gefur því kvaðningu og ChatGPT mun finna, greina og sameina hundruðir netheimilda til að búa til yfirgripsmikla skýrslu á sama stigi og rannsóknargreiningaraðili. Knúið af útgáfu af væntanlegu OpenAI o3 líkani sem er fínstillt fyrir vefskoðun og gagnagreiningu, nýtir það rökhugsun til að leita, túlka og greina mikið magn af texta, myndum og PDF skjölum á internetinu, og aðlagar sig eftir þörfum til að bregðast við upplýsingum sem það rekst á.

Hæfnin til að samþætta þekkingu er forsenda fyrir því að búa til nýja þekkingu. Vegna þess að rökstyðja markar ítarlegar rannsóknir verulegt skref í átt að víðtækara markmiði okkar að þróa AGI, sem við höfum lengi séð fyrir okkur sem fær um að framleiða nýstárlegar vísindarannsóknir.

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:

  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 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.

Svona virkar þetta

Ítarlegar rannsóknir voru þjálfaðar með end-to-end styrkingarnámi með erfiðum vafra- og rökhugsunarverkefnum á ýmsum lénum. Í gegnum þá þjálfun lærði það að gera áætlun og framkvæma margþrepa ferli til að finna þau gögn sem það þarf, fara til baka og bregðast við rauntímaupplýsingum eftir þörfum. Líkanið getur einnig vafrað yfir skrár sem notendur hlaða upp, teiknað og unnið með línurit með Python-tólinu, fellt inn bæði myndrit sem það býr til og myndir frá vefsíðum í svörum sínum, og vitnað í tilteknar setningar eða kafla úr heimildum sínum. Sem afleiðing af þessari þjálfun nær hún nýjum hæðum í fjölda opinberra mats sem beinast að raunverulegum vandamálum.

Síðasta próf mannkynsins

Á Síðasta prófi mannkynsins⁠(opnast í nýjum glugga), nýútgefnu mati sem prófar gervigreind á fjölbreyttum sviðum með spurningum á sérfræðistigi, nær líkanið sem knýr ítarlegar rannsóknir nýju hámarki með 26,6% nákvæmni. Þetta próf inniheldur yfir 3.000 fjölvalsspurningar og stutt svör yfir meira en 100 fög, frá málvísindum til eldflaugafræði, klassískum fræðum til vistfræði. Í samanburði við OpenAI o1 komu mestu framfarirnar í efnafræði, hugvísindum, félagsvísindum og stærðfræði. Líkanið sem knýr djúpar rannsóknir sýndi mannlíka nálgun með því að leita sérhæfðra upplýsinga á áhrifaríkan hátt þegar þess er þörf.

LíkanNákvæmni (%)
GPT-4o3,3
Grok-23,8
Claude 3.5 Sonnet4,3
Gemini Thinking6,2
OpenAI o19,1
DeepSeek-R1*9,4
OpenAI o3-mini (miðlungs)*10,5
OpenAI o3-mini (hátt)*13,0
Ítarleg rannsókn á OpenAI**26,6
* Líkanið er ekki fjölþætt, metið á hlutmengi einungis með texta.
**með vafra + Python-verkfærum

GAIA

Á GAIA⁠(opnast í nýjum glugga)1, opinberu viðmiði sem metur gervigreind byggt á raunverulegum spurningum, nær líkanið sem knýr ítarlegar rannsóknir nýjum hæðum og trónir efst á ytri stigatöflu⁠(opnast í nýjum glugga). Með spurningum á þremur erfiðleikastigum krefst árangursrík lausn þessara verkefna hæfileika eins og rökhugsun, fjölþætt flæði, vefvafra og verkfæranotkun.

GAIA
Stig 1Stig 2Stig 3Meðaltal
Fyrri SOTA(opnast í nýjum glugga)67,9267,4442,3163,64
Ítarleg rannsókn (pass@1)74,2969,0647,667,36
Ítarleg rannsókn (cons@64)78,6673,2158,0372,57

Dæmi um GAIA verkefni

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.

[leitar aðgerð]

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.

[vafra aðgerð]

Reflecting on potential issues

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

[vafra aðgerð]

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.

[leitar aðgerð]

Pinpointing key features

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

[vafra aðgerð]

Piecing together insights

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

[leitar aðgerð]

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.

[leitar aðgerð]

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.

[leitar aðgerð]

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.

[vafra aðgerð]

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.

[vafra aðgerð]

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."

[vafra aðgerð]

Evaluating library sources

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

[vafra aðgerð]

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.

[vafra aðgerð]

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.

[vafra aðgerð]

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.

[vafra aðgerð]

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.

[leitar aðgerð]

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.

[leitar aðgerð]

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.

[leitar aðgerð]

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.

[leitar aðgerð]

Sérfræðiverkefni

Í innra mati á verkefnum á sérfræðistigi á ýmsum sviðum mátu sérfræðingar í lénum það svo að ítarlegar rannsóknir hefðu sjálfvirknivætt margar klukkustundir af erfiðri, handvirkri rannsóknarvinnu. 

Árangurshlutfall miðað við hámarks verkfæraköll
ÁrangurshlutfallMax verkfæraköll
Því meira sem líkanið vafrar og hugsar um það sem það er að vafra um, því betur gengur því, þess vegna er mikilvægt að gefa því tíma til að hugsa.
Dæmi um verkefni á sérfræðistigi
Tími sem sparaðist við verkefni: 4 klukkustundir

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

Here kDk_D is the Henry’s law coefficient for dissolution, CH′C'_H is the Langmuir capacity constant (proportional to the polymer’s unrelaxed free-volume or “hole” capacity), and bb is the Langmuir affinity constant. This empirical formula yields the characteristic concave isotherm of glassy polymers and reduces to simple Henry’s law at the glass-transition (since CH′→0C'_H \to 0 as T→TgT \to T_g). The same three parameters (kD,CH′,bk_D, C'_H, b) are obtained by fitting pure-gas sorption data and are then used to predict mixture sorption. For a binary mixture of gases A and B (with partial pressures pA,pBp_A, p_B), the DMS model assumes each gas obeys its own Henry’s term and that both compete for the same Langmuir sites. One convenient form is to write the total sorbed concentration as the sum of contributions from each gas:

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

which implicitly means the concentration of component i in the mixture is Ci=kD,ipi+CH,i′bipi 1+∑jbjpj C_i = k_{D,i} p_i + \frac{C'_{H,i} b_i p_i}{\,1+\sum_j b_j p_j\,}. This reduces to the single-gas formula when the other component’s pressure is zero. In practice, partial pressure in these equations is often replaced by fugacity for accuracy at high pressures, though using fugacities or pressures gives nearly the same results if applied consistently. The DMS mixed-gas model predicts the competitive sorption effect observed experimentally – namely, each gas’s uptake is depressed relative to its pure-gas sorption isotherm.

Challenges and limitations: Although the dual-mode model is widely successful in correlating sorption data, it has notable limitations in predicting sorption behavior. First, the DMS model is empirical – its fitted parameters are not universal constants but can vary with polymer formation history, physical aging, and temperature/pressure range. This means a parameter set obtained under one set of conditions may lack predictive power outside its calibration range. For instance, sorption isotherms measured only over a limited pressure range can be fit well by DMS, but extrapolating beyond that (especially approaching plasticization pressures) may be unreliable. Indeed, multiple distinct combinations of kDk_D, CH′C'_H, bb can sometimes fit the same pure-gas isotherm almost equally well – yet they yield markedly different mixed-gas predictions. This sensitivity undermines confidence in mixture forecasts, as one study showed that using different pure-gas fit sets led to only qualitative agreement with some mixed-CO₂/CH₄ data.

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.

Öðlast skilning á soglíkönum

Ég er að safna upplýsingum um hreint og blandað gassorpingu í glerkenndum fjölliðum, með áherslu á tvíhama soglíkanið og tekst á við áskoranir í líkanaspám.

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Met opinn aðgang

Allt í lagi, leyfðu mér að sjá. Niðurstaðan frá MDPI um sog blandaðs gass í glerkenndum fjölliðum er líklega með opnum aðgangi.

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Skoðun á köflum
Ég er að draga saman innsýn úr tvíhama soglíkani og ítarlegri greiningu þess í MDPI-greininni.

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Skýri lykileiginleika

Ég er að skoða rannsóknarskýrsluna til að skilja fastann í lögmáli Henrys, sæknifasta Langmuirs og afkastagetufasta Langmuirs, sem lýsa gassogi.

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Púsla saman

Ég er að skoða möguleikann á að "Jafna (3)" og "Jafna (4)" gætu verið myndir eða formúlubirtingar og íhuga að skoða PDF eða leita að fyrri tilvísunum til að skilja þær betur.

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Staðfesti gagnsemi titils

Ég er að staðfesta hvort skjalið frá Freeman og Koros (1980) passi nákvæmlega við tilvísunina "Líkan fyrir sog á blönduðum lofttegundum í glerkenndum fjölliðum" með því að skoða ágrip þess og titil.

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Skoða tengingar greina

Ég tók eftir því að niðurstaðan frá „pmc.ncbi.nlm.nih.gov“ er líklega sama MDPI greinin og mig langar að sjá hvort hún sýni textann eða einfaldaða útgáfu.

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Safna upplýsingum

Ég er að safna upplýsingum um hreint og blandað gassog í glerkenndum fjölliðum. Þetta er mikilvægt skref í að skilja hegðun efnisins undir mismunandi gasumhverfum.

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Ber saman gassog

Ég tók eftir því að nærvera koltvísýrings í blöndu hefur áhrif á upptöku metans í fjölliðu, sem dregur úr upptöku metans samanborið við hreint gas.

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Tengi hugtök

Ég er að velta fyrir mér hvernig hugtakið „stækkuð tvíhama jöfnublanda“ gæti tengst jöfnum fyrir tvíhama ofurleiðandi kapla. Þetta sýnir möguleika á að tengja hugtök milli mismunandi sviða.

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Óska eftir upplýsingum um yfirferð

Ég er að taka eftir beiðninni um að fara yfir „Kenningar um sog og flutning í fjölliðuhimnu“.

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Athuga PDF tengill

Ég er að forvitnast um Niðurstöðu [2] og skoða útdráttinn, sem felur í sér að stækka tvihama soglíkanið í tvíefna blöndu og tjá styrk A með tilliti til hlutþrýstings og fasta.

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Spái fyrir um soghegðun

Tvíhama líkanið gerir ráð fyrir að það að bæta við öðru gasi minnki sog við stöðugan hlutþrýsting, ólíkt sviðsmyndum með hreinu gasi.

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Sæki PDF

Ég er að safna upplýsingum úr PDF skjali á nature.com, hugsanlega eldri grein eftir Yampol'ski, en er að hugsa um aðrar heimildir eins og gagnagrunn Háskólans í Texas í Austin fyrir textabút.

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Met valkosti fyrir geymslur

Ég hef komist að því að niðurstaðan úr UT-geymslunni gæti birst á rangan hátt. Það er óvíst að leita innan þess. Að auki inniheldur niðurstaða [3] brot sem lýsir tvíþættu blöndusogslíkani, útskýrir samkeppnisáhrif og hæfni þess til að líkja eftir tilraunagögnum.

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Fínpússa fókusinn

Ég er að aðlaga nálgun mína til að tryggja að leitin miði að réttu samhengi.

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Öðlast skilning á soglíkönum

Ég er að útskýra tvíhátta soglíkan fyrir hreinar lofttegundir: Henry uppleysing og Langmuir örholufylling. Fyrir blandaðar lofttegundir er lögð áhersla á samkeppnissorp og ófullkomleika.

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Kanna hero.epa.gov

Hugsa um hero.epa.gov mögulegt útdráttur eða innihald.

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Fínpússa líkanið

Ég er að safna innsýn um hreint og blandað gassog, með hliðsjón af þáttum eins og samkeppni, mýkingu og stöðugleika breytna. Að fella inn gögn um blandaðar lofttegundir til að fá nákvæmari spár.

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Tekst á við áskoranir í tvöföldu sogunarlíkani

Spár um tengingu breytna og áhrif óeinsleitni. Skortur á breytingum á ástandi fjölliða gæti leitt til yfirsjóna.

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Magngreini frávik

Ég er að magngreina ófullkomna leysni CO2-CH4 í 6FDA-mPDA og sýna blönduð áhrif gass á leysni. Samkeppnisbundin sorption er ekki eina orsök lækkunar á gegndræpissértækni.

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Felli inn tilvísanir

Ég er að safna tilvísunum, með áherslu á opið efni eins og MDPI 2019 og Vopicka 2014, til að styðja við umræðuna um mismun og áskoranir í tvíhama líkaninu fyrir gassog í fjölliðuhimnum.

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Legg til kafla

Ég er að skipuleggja kafla um hreint og blandað gassog, kanna tvíþætta soglíkanið og útskýra áskoranir eins og óvissu í breytum og spá fyrir um blandað gas.

Skipulegg efni

Ég er að einbeita mér að tvíþætta soglíkaninu, útskýri hreina og blandaða gassorgshegðun og undirstrika helsta mismun og áskoranir með punktum og skipulögðum köflum til skýringar.

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Vel tilvitnanir

Allt í lagi, leyfðu mér að sjá. Ég treysti á [3], [20], [22] og [21] fyrir skýra innsýn þeirra í tvívirka sorption og gasblöndur.

Kortlegg breytur

Ég er að setja saman breytunöfn til að forðast rugling. Ég held mig við (k), (C_H') og (b) til skýrleika. Jöfnurnar fyrir hreint gas og blöndu eru ítarlegar og skýrt uppsettar.

Tekst á við áskoranir

Ég er að takast á við fjöllausnabreytuaðlögun, mýkingu fjölliða og skammvinn áhrif. Þessar flækjur gera það erfitt að spá fyrir um hreina og blandaða gassorp.

Púsla saman

Ég er að skipuleggja kafla um mismun á sogi, tvíþætt líkan fyrir hreinar lofttegundir, útvíkkun þess fyrir blandaðar lofttegundir og tengdar áskoranir. Þessi þróun virðist rökrétt og árangursrík.

Árangurshlutfall sérfræðiverkefna eftir áætluðu efnahagslegu virði
ÁrangurshlutfallÁætlað efnahagslegt gildi
Árangurshlutfall verkefna á sérfræðistigi miðað við áætlaðar klukkustundir
ÁrangurshlutfallÁætlaðar klukkustundir
Áætlað efnahagslegt gildi verkefnis er meira tengt árangurshlutfalli en fjölda klukkustunda sem það tæki manneskju – það sem líkönum finnst erfitt er frábrugðið því sem manneskjur telja tímafrekt.

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.

Hvað er á döfinni

Ítarlegar rannsóknir eru í boði í dag á ChatGPT‑vefnum og verða kynntar í farsíma- og borðtölvuöppum í þessum mánuði. Eins og er getur ítarleg rannsókn nálgast opna vefinn og allar upphlaðnar skrár. Í framtíðinni munt þú geta tengja við sérhæfðari gagnaveitur—með því að auka aðgang að áskriftar- eða innri auðlindum—til að gera frálag þess enn öflugra og persónulegra.

Þegar fram í sækir sjáum við fyrir okkur að sjálfráðar upplifanir komi saman í ChatGPT fyrir ósamstilltar, raunverulegar rannsóknir og framkvæmd. Samsetningin af ítarlegum rannsóknum, sem geta framkvæmt ósamstillta rannsókn á netinu, og Operator, sem getur gripið til raunverulegra aðgerða, mun virkja ChatGPT til að framkvæma sífellt háþróaðri verkefni fyrir þig.

Viðbót 3. febrúar 2025: Við gerðum strangar öryggisprófanir, viðbúnaðarmat og endurskoðuðum stjórnkerfi á fyrstu útgáfu o3 sem knýr ítarlegar rannsóknir og skilgreindum hana sem miðlungs⁠(opnast í nýjum glugga) áhættu. Við keyrðum einnig viðbótaröryggisprófanir til að skilja betur stigvaxandi áhættu í tengslum við getu ítarlegra rannsókna til að vafra um vefinn, og við höfum bætt við nýjum mótvægisaðgerðum. Við munum halda áfram að prófa rækilega og fylgjast náið með núverandi takmarkaðri útgáfu. Við munum deila innsýn okkar í öryggismál og öryggisráðstafanir fyrir ítarlegar rannsóknir á kerfiskorti þegar við opnum aðgang fyrir Plus notendur.

Footnotes

  1. 1

    We found that the ground-truth answers for this dataset were widely leaked online and have blocked several websites or URLs accordingly to ensure a fair evaluation of the model.

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