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Record W4309034802 · doi:10.1021/accountsmr.2c00154

Uniting Heat and Light in Heterogeneous CO<sub>2</sub> Photocatalysis: Optochemical Materials and Reactor Engineering

2022· article· en· W4309034802 on OpenAlexafffund
Junchuan Sun, Wei Sun, Lu Wang, Geoffrey A. Ozin

Bibliographic record

VenueAccounts of Materials Research · 2022
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversity of Toronto
FundersZhejiang UniversityUniversity of TorontoMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of ChinaNatural Science Foundation of Shenzhen City
KeywordsPhotothermal therapyCatalysisPhotocatalysisOxideNanotechnologyIndiumChemistryPhotothermal effectMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Conspectus The rapid increase in atmospheric CO 2 concentration (∼420 ppm) has become one of the significant issues threatening human survival. As an effective measure to solve this major problem, renewable energy-powered catalytic CO 2 conversion technologies have received vast attention in both academia and industry. Among these techniques, photothermal catalysis is a rising star with promising potential for CO 2 conversion even under milder conditions. Indium oxide was among the first to be used in photothermal CO 2 catalysis, and through its various forms, stoichiometries, and surface chemistry, it has become one of the most well-studied photothermal catalyst systems. Indium oxide is a highly tunable semiconductor for CO 2 photocatalysis, which can be driven by both light photochemically and heat photothermally, thereby serving as an archetype for understanding how to optimize its performance for storing solar as chemical energy, through creative materials chemistry. Our solar fuel cluster discovered photothermal CO 2 catalysis over indium oxide in 2014 and has long been committed to the study of this field. Photothermal catalysis by semiconductors like indium oxide can be deconstructed into three key processes: photochemistry, thermochemistry, and surface chemistry. To be specific, photoexcited electron–hole pairs can enable redox and acid–base surface chemical reactions. Phonons and plasmons can drive these reactions photothermally. Surface active sites, such as surface frustrated Lewis pairs and oxygen vacancies, can amplify product activity and selectivity. Designer synergism between all of these effects ultimately determines the overall performance metrics of photothermal CO 2 catalysis. Thus, to design and optimize a photothermal catalyst, the three aforementioned key processes should be considered synergistically. In this Account, indium oxide-based catalysts are selected as an archetype to introduce the process of photothermal CO 2 catalysis and the advancements of indium oxide-based catalysts mainly from our solar fuel cluster are summarized. In detail, the strategies of material design are introduced systematically with the three key processes: photochemistry, thermochemistry, and surface chemistry. Moreover, a foreseeable future of the emerging field of optochemical engineering of photothermal catalysis, ranging from potential reactions to reactor design, is included as the perspective as well. In other words, this Account is dedicated to exploring how chemically tailored indium oxide-based catalyst has served as a platform material for understanding photothermal CO 2 catalysis and how this know-how is enabling the design of high quantum efficiency photocatalysts and photoreactors. A comprehensive understanding of these points is the key to the development of the emerging field of optochemical materials and reactor engineering of heterogeneous CO 2 photocatalysis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.312
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations44
Published2022
Admission routes2
Has abstractyes

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