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Record W2416752546 · doi:10.11648/j.ajche.20160402.16

Converting Volatile Organic Compounds to CO<sub>2</sub> and Water

2016· article· en· W2416752546 on OpenAlexaff
Tang G. Lee

Bibliographic record

VenueAmerican Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of Calgary
FundersSouth China University of Technology
KeywordsPhotocatalysisEnvironmental chemistryChemistryCatalysisUltravioletEnvironmental sciencePulp and paper industryMaterials scienceOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

This paper discusses a novel method to clean VOC from any sources. Photocatalysis surpasses any other technologies in air purification for absorption of volatile organic compounds. A key component is the use of ultraviolet light to activate a photo-catalysis process that can also disinfect microorganisms including bacteria, viruses, moulds and even infectious diseases. The by-products of the photocatalytic process is only carbon dioxide and water. Results as tested by the Chinese government agency for its VOC removal efficacy at several factories is most encouraging. In conclusion, we have found a technology that can help clean the increasing air pollution in China to improve public health.

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 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 score0.300
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.251
Teacher spread0.236 · 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.

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

Citations5
Published2016
Admission routes1
Has abstractyes

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