2. februar 2025.

Predstavljanje dubinskog istraživanja

Agent koji koristi rezonovanje da sintetizira velike količine online informacija i izvrši višestepene istraživačke zadatke za vas. Dostupno danas Pro korisnicima, a uskoro i Plus i Team korisnicima.

Isprobajte ChatGPT

Učitavanje…

Ažuriranje 10. februara 2026: Sada možete povezati duboko istraživanje sa bilo kojim MCP-om ili aplikacijom i ograničiti web pretrage na pouzdane stranice, tako da se možete fokusirati na autentificirane, industrijski standardne izvore. Takođe možete pratiti napredak u stvarnom vremenu i prekinuti kako biste usavršili rad dodatnim upitima ili novim izvorima. Ažurirali smo vizuelno iskustvo kako bi bilo lakše započeti, pratiti i pregledati vaše istraživanje od početka do kraja.

Ažuriranje od 17. jula 2025.: Duboko istraživanje sada može ići još dublje i šire uz pristup vizualnom pretraživaču kao dijelu ChatGPT agenta. Da biste pristupili ovim ažuriranim mogućnostima, jednostavno odaberite „agent mod” iz padajućeg menija u editoru i direktno unesite svoj upit. Izvorna funkcionalnost dubokog istraživanja ostaje dostupna putem opcije “duboko istraživanje” u meniju alata.

Ažuriranje od 24. aprila 2025:: Značajno povećavamo učestalost korištenja dubokog istraživanja—korisnici Plus, Team, Enterprise i Edu sada dobijaju 25 upita mjesečno, Pro korisnici 250, a Free korisnici 5. Ovo je omogućeno novom laganom verzijom dubokog istraživanja koju pokreće verzija o4-mini, dizajnirana da bude isplativija uz očuvanje visokog kvaliteta. Kada dostignete ograničenje za punu verziju, vaši upiti će se automatski prebaciti na laganu verziju.

Ažuriranje 25. februara 2025.: Svi Plus korisnici sada mogu koristiti duboko istraživanje.

Ažuriranje 5. februara 2025.: Duboko istraživanje je sada dostupno Pro korisnicima u Ujedinjenom Kraljevstvu, Švicarskoj i Evropskom ekonomskom prostoru.

Danas pokrećemo duboko istraživanje u ChatGPT‑u, novoj agentskoj mogućnosti koja provodi višestepena istraživanja na internetu za složene zadatke. Za desetak minuta postiže ono što bi čovjeku trebalo mnogo sati.

Duboko istraživanje je sljedeći agent OpenAI-ja koji može samostalno raditi za vas — date mu upit, a ChatGPT će pronaći, analizirati i sintetizirati stotine online izvora kako bi kreirao sveobuhvatan izvještaj na nivou istraživačkog analitičara. Pokretan verzijom nadolazećeg OpenAI o3 modela optimiziranog za pregledavanje weba i analizu podataka, koristi rezonovanje za pretraživanje, interpretaciju i analizu ogromnih količina teksta, slika i PDF-ova na internetu, prilagođavajući se prema potrebi kao reakcija na informacije na koje naiđe.

Sposobnost sinteze znanja je preduslov za kreiranje novog znanja. Iz tog razloga, duboko istraživanje označava značajan korak ka našem širem cilju razvoja AGI, koju smo dugo zamišljali kao sposobnu za proizvodnju novih naučnih istraživanja.

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.

Kako to radi

Dubinsko istraživanje je obučeno korištenjem end-to-end pojačanog učenja na zahtjevnim zadacima pretraživanja i rezonovanja u različitim domenama. Kroz tu obuku, naučilo je planirati i izvršavati višekoračnu putanju kako bi pronašlo potrebne podatke, vraćajući se unatrag i reagirajući na informacije u stvarnom vremenu gdje je to potrebno. Model također može pretraživati datoteke koje su korisnici otpremili, prikazivati i iterirati grafove koristeći Python alat, ugraditi generirane grafikone i slike s web stranica u svoje odgovore, te citirati konkretne rečenice ili odlomke iz svojih izvora. Kao rezultat ove obuke, postiže nove vrhunce u brojnim javnim evaluacijama usmjerenim na probleme iz stvarnog svijeta.

Posljednji ispit čovječanstva

Na Humanity’s Last Exam⁠(otvara se u novom prozoru), nedavno objavljenoj evaluaciji koja testira umjetnu inteligenciju u širokom spektru tema na pitanjima na nivou stručnjaka, model koji pokreće duboko istraživanje postiže novi rekord od 26,6% tačnosti. Ovaj test se sastoji od više od 3.000 pitanja s višestrukim izborom i kratkim odgovorima iz više od 100 predmeta, od lingvistike do raketne nauke, od klasika do ekologije. U poređenju s OpenAI o1, najveći dobici su zabilježeni u hemiji, humanističkim i društvenim naukama, te matematici. Model koji pokreće duboka istraživanja pokazao je pristup sličan ljudskom, efikasno tražeći specijalizirane informacije kada je to potrebno.

Model	Tačnost (%)
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 (high)*	13,0
OpenAI dubinsko istraživanje	26,6

* Model nije multimodalan, evaluiran je na podskupu koji sadrži samo tekst.

**s pretraživanjem i Python alatima

GAIA

Na GAIA⁠(otvara se u novom prozoru)¹, javnom benchmarku koji procjenjuje umjetnu inteligenciju na stvarnim pitanjima, model koji pokreće duboko istraživanje dostiže novi nivo dostignuća (SOTA), zauzimajući vrh eksterne rang liste⁠(otvara se u novom prozoru). Obuhvatajući pitanja na tri nivoa težine, uspješno završavanje ovih zadataka zahtijeva sposobnosti kao što su rezonovanje, multimodalna fluentnost, pretraživanje interneta i vještina korištenja alata.

GAIA
	Nivo 1	Nivo 2	Nivo 3	Prosjek.
Prethodno SOTA(otvara se u novom prozoru)	67,92	67,44	42,31	63,64
Dubinsko istraživanje (pass@1)	74,29	69,06	47,6	67,36
Dubinsko istraživanje (cons@64)	78,66	73,21	58,03	72,57

Primjeri zadataka 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.

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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

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

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Zadaci stručne razine

U internoj evaluaciji zadataka na nivou stručnjaka u različitim oblastima, stručnjaci iz domena su ocijenili da je duboko istraživanje automatizovalo više sati teškog, ručnog istraživanja.

Stopa prolaznosti u odnosu na maksimalni broj poziva alata

Što više model pretražuje i razmišlja o onome što pretražuje, to bolje radi, zbog čega je važno dati mu vremena za razmišljanje.

Primjeri zadataka na ekspertskom nivou

Sačuvanog vremena na zadatku: 4 sata

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.

Understanding sorption models

I’m gathering info on pure and mixed-gas sorption in glassy polymers, focusing on the dual-mode sorption model and addressing challenges in modeling predictions.

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Assessing open access

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

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Ispitivanje sekcija
Prikupljam uvide iz Modela dualne sorpcije i njegove proširene analize u MDPI radu.

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Pojašnjenje ključnih svojstava

Pregledavam laboratorijski izvještaj kako bih razumio Henryjevu konstantu zakona, Langmuirovu konstantu afiniteta i Langmuirovu konstantu kapaciteta, koje opisuju sorpciju plina.

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Spajanje

Razmatram mogućnost da „Jednačina (3)" i „Jednačina (4)" mogu biti slike ili prikazi formula i razmišljam o konsultovanju PDF-a ili pretraživanju ranijih referenci kako bih ih bolje razumio.

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Potvrđivanje relevantnosti naslova

Potvrđujem da li se dokument Freemana i Korosa (1980) specifično podudara s referencom „Model za sorpciju miješanih plinova u staklastim polimerima“ provjerom njegovog sažetka i naslova.

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Ispitivanje povezanosti članaka

Primijetio sam da je rezultat sa „pmc.ncbi.nlm.nih.gov“ vjerovatno isti MDPI članak i zanima me da li prikazuje tekst ili pojednostavljenu verziju.

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Prikupljanje informacija

Prikupljam informacije o sorpciji čistih i miješanih plinova u staklastim polimerima. Ovo je ključni korak u razumijevanju ponašanja materijala u različitim plinskim okruženjima.

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Usporedba sorpcije plinova

Primijetio sam da prisustvo ugljikovog dioksida u smjesi utiče na sorpciju metana u polimeru, smanjujući sorpciju metana u poređenju sa scenarijem čistog plina.

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Povezivanje koncepata

Razmišljam o tome kako bi se termin „proširena mješavina jednačina dvostrukog moda” mogao povezati s jednačinama dvostrukog moda supravodljivog kabla. Ovo pokazuje potencijal za povezivanje koncepata kroz različite oblasti.

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Zahtjev za detalje pregleda

Bilježim zahtjev za pregled „Teorije sorpcije i transporta u polimernim membranama”.

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Provjeravanje PDF poveznice

Zanima me Rezultat [2] i istražujem isječak, koji uključuje proširenje modela sorpcije dvostrukog načina na binarnu smjesu i izražavanje koncentracije A u terminima parcijalnih pritisaka i konstanti.

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Predviđanje ponašanja sorpcije

Dvomodni model predviđa da dodavanje drugog plina smanjuje sorpciju pri konstantnom parcijalnom tlaku, za razliku od scenarija s čistim plinovima.

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Preuzimanje PDF-a

Prikupljam informacije o PDF dokumentu sa nature.com, moguće o starijem članku Yampol'skog, ali razmišljam o alternativnim izvorima poput repozitorija Univerziteta u Teksasu u Austinu za isječak teksta.

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Procjena opcija repozitorija

Otkrio sam da se rezultat UT repozitorija možda ne prikazuje ispravno. Pretraga unutar toga je neizvjesna. Alternativno, rezultat [3] sadrži isječak koji opisuje dvomodni model sorpcije smjese, objašnjavajući konkurentske efekte i njegovu adekvatnost u modeliranju eksperimentalnih podataka.

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Usavršavanje fokusa

Prilagođavam svoj pristup kako bih osigurao da pretraga cilja pravi kontekst.

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Razumijevanje modela sorpcije

Detaljno opisujem model sorpcije sa dvostrukim načinom rada za čiste plinove: Henryjevo otapanje i Langmuirovo punjenje mikrošupljina. Za miješane plinove, naglašena je konkurentna sorpcija i neidealnosti.

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Istražiti hero.epa.gov

Razmišljanje kroz hero.epa.gov potencijalni sažetak ili sadržaj.

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Usavršavanje modela

Prikupljam uvide o sorpciji čistih i miješanih plinova, uzimajući u obzir faktore kao što su konkurencija, plastifikacija i stabilnost parametara. Uključivanje podataka o miješanim plinovima za preciznija predviđanja.

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Rješavanje izazova dvomodnog modela sorpcije

Predviđanja utjecaja spajanja parametara i nejedinstvenosti. Izostanak promjena u stanju polimera može dovesti do previdâ.

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Kvantificiranje odstupanja

Kvantificiram neidealnu rastvorljivost CO2-CH4 u 6FDA-mPDA, pokazujući uticaj miješanih plinova na rastvorljivost. Konkurentna sorpcija nije jedini uzrok opadanja selektivnosti propusnosti.

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Uključivanje referenci

Prikupljam citate, fokusirajući se na materijale otvorenog pristupa poput MDPI 2019 i Vopicka 2014, kako bih podržao diskusiju o razlikama i izazovima u dualnom modelu za sorpciju plina u polimernim membranama.

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Predlaganje odjeljaka

Organiziram odjeljke o sorpciji čistog i miješanog plina, istražujem dvomodni model sorpcije i detaljno opisujem izazove poput nesigurnosti parametara i predviđanja miješanog plina.

Organizacija sadržaja

Fokusiram se na model sorpcije s dvostrukim načinom rada, detaljno opisujući ponašanje sorpcije čistih i miješanih plinova, te ističući ključne razlike i izazove pomoću nabrajanja i strukturiranih odjeljaka radi jasnoće.

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Odabir citata

OK, da vidim. Oslanjam se na [3], [20], [22] i [21] zbog njihovih jasnih uvida u dvostruki način sorpcije i plinske smjese.

Mapiranje parametara

Sastavljam nazive parametara kako bih izbjegao zabunu. Radi jasnoće, ostajemo pri (k), (C_H') i (b). Jednačine za čisti gas i smjesu su detaljno i jasno formatirane.

Rješavanje izazova

Bavim se prilagođavanjem parametara višestrukih rješenja, plastifikacijom polimera i vremenskim efektima. Ove složenosti čine predviđanje sorpcije čistog i miješanog plina izazovnim.

Spajanje

Strukturiram odjeljke o razlikama u sorpciji, modelu dvostrukog načina za čiste plinove, njegovom proširenju na miješane plinove i povezanim izazovima. Ovaj napredak čini se logičnim i učinkovitim.

Stopa prolaznosti na zadacima stručnog nivoa prema procijenjenoj ekonomskoj vrijednosti

Stopa prolaznosti na zadacima stručnog nivoa prema procijenjenim satima

Procijenjena ekonomska vrijednost zadatka je više povezana sa stopom prolaznosti nego sa brojem sati koji bi bili potrebni čovjeku – stvari koje modeli smatraju teškim razlikuju se od onoga što ljudima oduzima mnogo vremena.

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.

Šta je sljedeće

Duboko istraživanje je danas dostupno na ChatGPT webu i bit će uvedeno na mobilne i desktop aplikacije u roku od mjesec dana. Trenutno, dubinsko istraživanje može pristupiti otvorenom webu i svim otpremljenim datotekama. U budućnosti ćete moći povezati se sa specijalizovanijim izvorima podataka—proširujući pristup resursima zasnovanim na pretplati ili internim resursima—kako biste njegov izlaz učinili još robusnijim i personalizovanijim.

Gledajući dalje u budućnost, predviđamo da će se agentička iskustva objediniti u ChatGPT‑u za asinhrono istraživanje i izvršavanje u stvarnom svijetu. Kombinacija dubinskog istraživanja, koje može obavljati asinhrono online istraživanje, i Operatora, koji može poduzeti radnje u stvarnom svijetu, omogućit će ChatGPT‑u da obavlja sve složenije zadatke za vas.

Dodatak od 3. februara 2025: Proveli smo rigorozna sigurnosna testiranja, evaluacije spremnosti i preglede upravljanja ranom verzijom o3 koja omogućava duboko istraživanje, identificirajući je kao srednji⁠(otvara se u novom prozoru) rizik. Također smo proveli dodatna sigurnosna testiranja kako bismo bolje razumjeli inkrementalne rizike povezane s mogućnošću dubokog istraživanja da pretraživati web, te smo dodali nove mjere ublažavanja. Nastavit ćemo temeljito testirati i pažljivo pratiti trenutno ograničeno izdanje. Podijelit ćemo naše sigurnosne uvide i zaštitne mjere za duboko istraživanje u sistemskoj kartici kada proširimo pristup Plus korisnicima.

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 i Zhiqing Sun

Foundational Contributors

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