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Record W2914392225 · doi:10.1002/cjce.23477

Recent advances in computational photocatalysis: A review

2019· review· en· W2914392225 on OpenAlexafffundvenue
Xiangchao Meng, Nan Ji Yun, Zisheng Zhang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typereview
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsPhotocatalysisComputationComputer scienceMaterials scienceBand gapCharge carrierBiochemical engineeringNanotechnologyComputational scienceProcess engineeringChemistryAlgorithmCatalysisEngineeringOptoelectronics

Abstract

fetched live from OpenAlex

Abstract Photocatalysis has been extensively developed in recent years. Along with the development of computational software and hardware technology, the idea of designing a novel material (such as photocatalytic materials) starting from computation/modelling methods is growing increasingly popular. The combination of computational methods and material design is known as material simulation. The most difficult task in material simulation is to solve the Schrödinger equation. Aiming to overcome this problem, a large variety of approaches have been implemented and a fundamental package has been developed, namely DFT. Based on the simulated results, the most of typical parameters in photocatalysis such as band gap, optical property, recombination of photogenerated charge carriers, as well as adsorption and its mechanism can be investigated and estimated. Moreover, other computation methods relevant to photocatalysis were also herein introduced, primarily including irradiation modelling (using RTE) in a photoreactor, mass transfer, photocatalytic reaction kinetics, and the evaluation of photocatalytic activity. Computation methods applied to photocatalysis using various fundamental and equations are reviewed with an emphasis on materials simulation using DFT.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.293
Teacher spread0.270 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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

Citations62
Published2019
Admission routes3
Has abstractyes

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