MétaCan
Menu
Back to cohort
Record W1566791157 · doi:10.1002/er.3211

A review on selected heterogeneous photocatalysts for hydrogen production

2014· review· en· W1566791157 on OpenAlexaff
Canan Acar, İbrahim Dinçer, Calin Zamfirescu

Bibliographic record

VenueInternational Journal of Energy Research · 2014
Typereview
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsPhotocatalysisHydrogen productionHydrogenProduction rateReactivity (psychology)ChemistryMaterials scienceCatalysisEngineeringOrganic chemistryProcess engineeringMedicine

Abstract

fetched live from OpenAlex

In this study, visible light-driven heterogeneous photocatalysts for hydrogen production are comparatively assessed based on technical, environmental, and cost criteria. The photocatalysis systems are compared with respect to their (i) rate of hydrogen generation per gram; (ii) rate of hydrogen generation per m2 of the specific surface area; and (iii) the band gap energy. The photocatalysis systems are also compared and discussed in terms of flammability, reactivity, and their impact on living systems' health. Furthermore, the costs of the required components of the photocatalysis systems are ranked. In addition to individual photocatalyst comparison, seven photocatalyst groups are ranked and compared. The results show that TiO2-C-362 and Ag0.03Mn0.40Cd0.60S show the highest in terms of µmol/h-gcat and µmol/h-m2cat, respectively, and TiO2-C-362 has the highest overall rankings. The Zn/In/S-based photocatalyst groups show the highest hydrogen production rate in terms of µmol/h-gcat and µmol/h-m2cat. Overall, Cd/S/Zn has the highest rankings when cost and health and environmental impact criteria are taken into account. Copyright © 2014 John Wiley & Sons, Ltd.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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.

Opus teacher head0.104
GPT teacher head0.465
Teacher spread0.361 · 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 designNot applicable
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

Citations210
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Energy ResearchSame topicAdvanced Photocatalysis TechniquesFrench-language works237,207