Recent advances in computational photocatalysis: A review
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".