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OpenAI

2 Februari 2025

Toa

Kutambulisha utafiti wa kina

Wakala anayetumia mantiki kuunganisha kiasi kikubwa cha habari mtandaoni na kukamilisha shughuli za utafiti wa hatua nyingi kwa ajili yako. Inapatikana kwa watumiaji wa Pro leo, Plus na Team inayofuata.

Inapakia…

Sasisho la Februari 10, 2026: Sasa unaweza kuunganisha utafiti wa kina na MCP au programu yoyote na kuzuia utafutaji wa wavuti kwenye tovuti zinazoaminika, ili uweze kuzingatia vyanzo vilivyothibitishwa, vya kiwango cha sekta. Pia sasa unaweza kufuatilia maendeleo kwa wakati halisi na kukatiza ili uboreshe kwa madokezo ya ufuatiliaji au vyanzo vipya. Tumesasisha uzoefu wa kuona ili iwe rahisi kuanza, kufuatilia, na kukagua utafiti wako kuanzia mwanzo hadi mwisho.


Sasisho la tarehe 17 Julai 2025: Utafiti wa kina sasa unaweza kwenda zaidi na kwa upana zaidi kwa ufikiaji wa kivinjari cha kuona kama sehemu ya wakala wa ChatGPT. Ili kufikia uwezo huu uliosasishwa, chagua tu "hali ya wakala" kutoka kwenye menyu kunjuzi ya mtunzi na uweke swali lako moja kwa moja. Utendaji asili wa utafiti wa kina bado unapatikana kupitia chaguo la "utafiti wa kina" kwenye menyu ya zana.


Sasisho la tarehe 24 Aprili 2025: Tunaongeza kwa kiasi kikubwa mara ngapi unaweza kutumia utafiti wa kina—Watumiaji wa Plus, Team, Enterprise, na Edu sasa wanapata maswali 25 kila mwezi, watumiaji wa Pro wanapata 250, na watumiaji wa Free wanapata 5. Hili linawezekana kupitia toleo jipya la utafiti wa kina linaloendeshwa na toleo la o4-mini, lililoundwa ili kuwa na gharama nafuu zaidi huku likihifadhi ubora wa juu. Ukifikia kikomo chako cha toleo kamili, maswali yako yatabadilika kiotomatiki hadi toleo jepesi.


Sasisho la Februari 25, 2025: Watumiaji wote wa Plus sasa wanaweza kutumia utafiti wa kina.


Sasisho la tarehe 5 Februari 2025: Utafiti wa kina sasa unapatikana kwa watumiaji wa Pro nchini Uingereza, Uswizi na Eneo la Kiuchumi la Ulaya.


Leo tunazindua utafiti wa kina katika ChatGPT, uwezo mpya wa wakala ambao unafanya utafiti wa hatua nyingi kwenye mtandao kwa shughuli ngumu. Inatimiza kwa dakika chache kile ambacho kingemchukua mwanadamu masaa mengi.

Utafiti wa kina ni wakala inayofuata wa OpenAI ambaye anaweza kukufanyia kazi kwa kujitegemea—unampa dokezo, na ChatGPT itapata, kuchambua, na kuunganisha mamia ya vyanzo vya mtandaoni ili kuunda ripoti ya kina katika kiwango cha mchambuzi wa utafiti. Ikiendeshwa na toleo lijalo la muundo wa OpenAI o3 ambalo limeboreshwa kwa ajili ya kuvinjari wavuti na uchanganuzi wa data, linatumia mantiki kutafuta, kutafsiri, na kuchanganua kiasi kikubwa cha maandishi, picha na PDF kwenye mtandao, likibadilika inavyohitajika ili kuitikia taarifa inayokutana nayo.

Uwezo wa kuunganisha maarifa ni sharti la unda maarifa mapya. Kwa sababu hii, utafiti wa kina unaashiria hatua muhimu kuelekea lengo letu pana la kuendeleza AGI, ambalo tumekuwa tukiliona kwa muda mrefu kama lenye uwezo wa kutoa utafiti mpya wa kisayansi.

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.

Jinsi inavyofanya kazi

Utafiti wa kina ulifundishwa kwa kutumia mafunzo ya uimarishaji wa mwisho hadi mwisho kwenye shughuli ngumu za kuvinjari na kufikiria katika vikoa mbalimbali. Kupitia mafunzo hayo, ilijifunza mpango na kutekeleza mchakato wa hatua nyingi ili kupata data inayohitaji, kurudi nyuma na kujibu taarifa za wakati halisi inapohitajika. Muundo huu pia unaweza kuvinjari faili zilizopakuliwa na mtumiaji, kuchora na kurudia kwenye grafu kwa kutumia zana ya Python, kupachika grafu zilizozalishwa na picha kutoka kwenye tovuti katika majibu yake na kunukuu sentensi au vifungu maalum kutoka kwenye vyanzo vyake. Kutokana na mafunzo haya, inafikia viwango vipya vya juu katika tathmini kadhaa za umma zinazolenga matatizo ya ulimwengu halisi.

Mtihani wa Mwisho wa Ubinadamu

Kwenye Mtihani wa Mwisho wa Ubinadamu(fungua katika dirisha jipya), tathmini iliyotolewa hivi karibuni ambayo hujaribu AI katika masomo mengi kwa maswali ya kiwango cha kitaalamu, muundo unaoendesha utafiti wa kina ulipata alama mpya ya juu ya usahihi wa 26.6%. Jaribio hili lina zaidi ya maswali 3,000 ya chaguo nyingi na majibu mafupi katika zaidi ya masomo 100 kutoka isimu hadi sayansi ya roketi, classics hadi ikolojia. Ikilinganishwa na OpenAI o1, mafanikio makubwa zaidi yalionekana katika kemia, masomo ya kibinadamu na sayansi ya jamii na hisabati. Muundo unaoendesha utafiti wa kina ulionyesha mbinu inayofanana na ya kibinadamu kwa kutafuta kwa ufanisi taarifa maalum inapohitajika.

MuundoUsahihi (%)
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
Utafiti wa kina wa OpenAI**26.6
* Muundo si wa multimodal, ulitathminiwa kwenye sehemu ndogo ya maandishi pekee.
**na kuvinjari + zana za Python

GAIA

Kwenye GAIA(fungua katika dirisha jipya)1, kipimo cha umma kinachotathmini AI kwenye maswali ya ulimwengu halisi, muundo unaoendesha utafiti wa kina unafikia hali mpya ya sanaa (SOTA), ukiongoza kwenye ubao wa wanaoongoza(fungua katika dirisha jipya) wa nje. Kujumuisha maswali katika viwango vitatu vya ugumu, kukamilisha kwa mafanikio shughuli hizi kunahitaji uwezo ikiwa ni pamoja na hoja, ufasaha wa njia nyingi, kuvinjari wavuti na ustadi wa matumizi ya zana.

GAIA
Kiwango cha 1Kiwango cha 2Kiwango cha 3Wastani.
SOTA ya awali(fungua katika dirisha jipya)67.9267.4442.3163.64
Utafiti wa kina (pass@1)74.2969.0647.667.36
Utafiti wa kina (cons@64)78.6673.2158.0372.57

Mifano ya shughuli za GAIA

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.

[tafuta kitendo]

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.

[kitendo cha vinjari]

Reflecting on potential issues

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

[kitendo cha vinjari]

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.

[tafuta kitendo]

Pinpointing key features

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

[kitendo cha vinjari]

Piecing together insights

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

[tafuta kitendo]

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.

[tafuta kitendo]

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.

[tafuta kitendo]

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.

[kitendo cha vinjari]

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.

[kitendo cha vinjari]

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."

[kitendo cha vinjari]

Evaluating library sources

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

[kitendo cha vinjari]

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.

[kitendo cha vinjari]

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.

[kitendo cha vinjari]

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.

[kitendo cha vinjari]

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.

[tafuta kitendo]

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.

[tafuta kitendo]

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.

[tafuta kitendo]

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.

[tafuta kitendo]

Shughuli za Kiwango cha Mtaalamu

Katika tathmini ya ndani ya shughuli za kiwango cha mtaalamu katika maeneo mbalimbali, utafiti wa kina ulitathminiwa na wataalamu wa kikoa kuwa umefanya otomatiki saa nyingi za uchunguzi mgumu wa mikono. 

Kiwango cha Kupita dhidi ya Wito wa Zana wa Juu Zaidi
Kiwango cha KufauluSimu za Zana za Max
Kadiri muundo unavyovinjari na kuweza kuzingatia kile kinachovinjari, ndivyo unavyofanya vizuri zaidi, ndiyo sababu kuupa muda wa kuzingatia ni muhimu.
Mifano ya shughuli za kiwango cha mtaalamu
Muda ulihifadhiwa kwenye shughuli: masaa 4

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.

Kuelewa miundo ya sorption

Ninakusanya maelezo kuhusu uvutaji wa gesi safi na mchanganyiko katika polima za glasi, nikilenga muundo wa uvutaji wa hali mbili na kushughulikia changamoto katika utabiri wa mifano.

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Kutathmini ufikiaji wa wazi

Sawa, wacha nione. Matokeo kutoka MDPI kuhusu uvutaji wa gesi mchanganyiko katika polima za glasi huenda ni ufikiaji wa wazi.

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Kuchunguza sehemu
Ninakusanya ufahamu kutoka kwa Muundo wa Dual Mode Sorption na uchambuzi wake uliopanuliwa ndani ya karatasi ya MDPI.

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Kufafanua sifa muhimu

Ninachunguza ripoti ya maabara ili kuelewa kigezo cha sheria ya Henry, kigezo cha uhusiano wa Langmuir, na kigezo cha uwezo wa Langmuir, ambavyo vinaelezea unyonyaji wa gesi.

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Kuweka pamoja

Ninashughulikia uwezekano kwamba "Equation (3)" na "Equation (4)" zinaweza kuwa picha au maonyesho ya fomula na ninazingatia kushauriana na PDF au kutafuta marejeleo ya awali ili kuyaelewa vyema.

[kitendo cha vinjari]

Kuthibitisha umuhimu wa kichwa

Ninathibitisha ikiwa hati kutoka kwa Freeman na Koros (1980) inalingana haswa na marejeleo "Muundo for sorption of mixed gases in glassy polymers" kwa kuangalia muhtasari na kichwa chake.

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Kuchunguza miunganisho ya makala

Niliona kuwa matokeo kutoka "pmc.ncbi.nlm.nih.gov" yanaweza kuwa nakala sawa ya MDPI, na ninavutiwa kuona ikiwa inawasilisha maandishi au toleo lililorahisishwa.

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Kukusanya taarifa

Ninakusanya taarifa kuhusu unyonyaji wa gesi safi na mchanganyiko katika polima za glasi. Hii ni hatua muhimu katika kuelewa jinsi nyenzo zinavyojitokeza chini ya mazingira tofauti ya gesi.

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Kulinganisha unyonyaji wa gesi

Niligundua kuwa uwepo wa kaboni dioksidi katika mchanganyiko huathiri utegaji wa methane kwenye polima, na hivyo kupunguza utegaji wa methane ikilinganishwa na hali ya gesi safi.

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Kuunganisha dhana

Ninafikiria jinsi neno "mchanganyiko wa milinganyo wa hali mbili uliopanuliwa" linaweza kuhusiana na milinganyo ya kebo ya hali mbili ya superconducting. Hii inaonyesha uwezekano wa kuunganisha dhana katika nyanja mbalimbali.

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Kuomba maelezo ya mapitio

Ninakubali ombi la kukagua "Nadharia za Sorption na Usafirishaji katika Membrani ya Polima."

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Kukagua kiungo cha PDF

Nina hamu ya kujua kuhusu Matokeo [2] na gundua kijisehemu, ambacho kinahusisha kupanua muundo wa hali mbili kwa mchanganyiko wa binary na kuelezea mkusanyiko wa A kwa kutumia shinikizo la sehemu na vigezo vya mara kwa mara.

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Kutabiri tabia ya unyonyaji

Muundo wa hali mbili unatarajia kwamba kuongeza gesi ya pili hupunguza unyonyaji kwa shinikizo la sehemu isiyobadilika, tofauti na hali za gesi safi.

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Kupakua PDF

Ninakusanya maelezo kwenye nature.com PDF, ikiwezekana nakala ya zamani ya Yampol'ski, lakini nikifikiria kuhusu vyanzo mbadala kama vile hazina ya Chuo Kikuu cha Texas huko Austin kwa kijisehemu cha maandishi.

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Kutathmini chaguzi za hazina

Nimegundua kuwa matokeo ya hazina ya UT yanaweza yasionyeshe ipasavyo. Kutafuta ndani yake ni jambo lisilo na uhakika. Vinginevyo, tokeo [3] lina kijisehemu kinachoelezea muundo wa uchanganyiko wa modi mbili wa sorption, unaoelezea athari za ushindani na utoshelevu wake katika kuiga data ya majaribio.

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Kuboresha umakini

Ninarekebisha mbinu yangu ili kuhakikisha utafutaji unalenga muktadha unaofaa.

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Kuelewa miundo ya sorption

Ninaelezea muundo wa sorption wa hali mbili kwa gesi safi: kuyeyusha kwa Henry na kujaza microvoid ya Langmuir. Kwa gesi mchanganyiko, sorption ya ushindani na kutokamilika kunasisitizwa.

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Kugundua hero.epa.gov

Kufikiria kupitia hero.epa.gov mukhtasari au maudhui yanayowezekana.

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Kurekebisha muundo

Ninakusanya maarifa kuhusu sorption ya gesi safi na mchanganyiko, nikizingatia mambo kama vile ushindani, plastiki na uthabiti wa vigezo. Kujumuisha data ya gesi mchanganyiko kwa utabiri sahihi zaidi.

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Kushughulikia changamoto za muundo wa sorption wa pacha

Uunganishaji wa vigezo na ubashiri wa athari zisizo za kipekee. Kutokuwepo kwa mabadiliko ya hali ya polima kunaweza kusababisha makosa ya uangalizi.

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Kuhesabu kupotoka

Ninakadiria umumunyifu usio bora wa CO2-CH4 katika 6FDA-mPDA, nikionyesha athari za gesi mchanganyiko kwenye umumunyifu. Uchukuaji wa ushindani sio sababu pekee ya kupungua kwa uteuzi wa upenyezaji.

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Kujumuisha kumbukumbu

Ninakusanya manukuu, nikilenga nyenzo za chanzo huria kama MDPI 2019 na Vopicka 2014, ili kutoa usaidizi wa mjadala kuhusu tofauti na changamoto katika muundo wa hali mbili wa unyonyaji wa gesi kwenye utando wa polima.

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Kupendekeza sehemu

Ninaandaa sehemu kuhusu kunyonya kwa gesi safi dhidi ya mchanganyiko, kuchunguza muundo ya kunyonya ya aina mbili na kufafanua changamoto kama vile kutokuwa na uhakika wa vigezo na ubashiri wa tabia ya gesi mchanganyiko.

Kupanga maudhui

Ninaangazia muundo wa sorption wa hali-mbili, nikielezea tabia za sorption ya gesi safi na mchanganyiko, na kuangazia tofauti kuu na changamoto kwa kutumia pointi za risasi na sehemu zilizopangwa kwa uwazi.

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Kuchagua nukuu

Sawa, wacha nione. Ninaegemea [3], [20], [22], na [21] kwa maarifa yao wazi kuhusu sorption ya hali mbili na mchanganyiko wa gesi.

Kuweka vigezo kwenye ramani

Ninaunganisha majina ya vigezo ili kuepusha mkanganyiko. Kushikamana na (k), (C_H') na (b) kwa uwazi. Milinganyo ya gesi safi na mchanganyiko zimeelezwa kwa kina na zimepangwa kwa uwazi.

Kushughulikia changamoto

Ninashughulikia upatanisho wa vigezo vya suluhisho nyingi, plastiki ya polima na athari za muda. Ugumu huu hufanya utabiri wa sorption ya gesi safi na mchanganyiko kuwa mgumu.

Kuweka pamoja

Ninapanga sehemu kuhusu tofauti za sorption, muundo wa hali-mbili wa gesi safi, upanuzi wake hadi gesi mchanganyiko na changamoto zinazohusiana. Maendeleo haya yanaonekana kuwa ya kimantiki na yenye ufanisi.

Kiwango cha Kufaulu katika Shughuli za Kiwango cha Utaalamu kwa Kadirio la Thamani ya Kiuchumi
Kiwango cha KufauluThamani ya Kiuchumi Inayokadiriwa
Kiwango cha Kufaulu katika Shughuli za Kiwango cha Mtaalam kwa Masaa Yanayokadiriwa
Kiwango cha KufauluSaa zilizokadiriwa
Thamani inayokadiriwa ya kiuchumi ya shughuli inahusiana zaidi na kiwango cha kufaulu kuliko idadi ya saa ambazo binadamu angehitaji – mambo ambayo miundo inapata kuwa magumu ni tofauti na yale ambayo wanadamu wanapata kuwa yanachukua muda.

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.

Kipi kinachofuata

Utafiti wa kina unapatikana leo kwenye tovuti ya ChatGPT, na utasambazwa kwa programu za simu na kompyuta za mezani ndani ya mwezi huu. Hivi sasa, utafiti wa kina unaweza kufikia wavuti wazi na faili zozote zilizopakizwa. Katika siku zijazo, utaweza kuunganisha kwenye vyanzo maalum zaidi vya data—kupanua ufikiaji wake wa rasilimali zinazotegemea usajili au za ndani—ili kufanya matokeo yake kuwa thabiti zaidi na ya kibinafsi.

Tukiangalia mbele zaidi, tunatarajia uzoefu wa kiwakala kuungana katika ChatGPT kwa ajili ya utafiti na utekelezaji usiolingana wa ulimwengu halisi. Mchanganyiko wa utafiti wa kina, ambao unaweza kufanya uchunguzi wa mtandaoni usiolingana, na Operator, ambaye anaweza kitendo katika ulimwengu halisi, utawasha ChatGPT kutekeleza shughuli zinazozidi kuwa za kisasa kwa ajili yako.


Nyongeza ya tarehe 3 Februari 2025: Tulifanya majaribio makali ya usalama, tathmini za kujiandaa, na hakiki za utawala kwenye toleo la awali la o3 ambalo linaendesha utafiti wa kina, na kulibainisha kuwa hatari ya Wastani(fungua katika dirisha jipya). Pia tulifanya majaribio ya ziada ya usalama ili kuelewa vyema hatari zinazoongezeka zinazohusiana na uwezo wa utafiti wa kina wa kuvinjari wavuti, na tumeongeza urekebishaji mpya. Tutaendelea kujaribu kwa kina na kufuatilia kwa karibu toleo lenye kikomo la sasa. Tutashiriki maarifa yetu ya usalama na hatua za ulinzi kwa ajili ya utafiti wa kina katika kadi ya mfumo tunapopanua ufikiaji kwa watumiaji wa Plus.

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 na 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 na Wojciech Zaremba