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Record W3164295008 · doi:10.1073/pnas.2105067118

Liquid crystal display screens as a source for indoor volatile organic compounds

2021· article· en· W3164295008 on OpenAlexaff
Qifan Liu, Jonathan P. D. Abbatt

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsUniversity of Toronto
FundersAlfred P. Sloan Foundation
KeywordsLiquid-crystal displayIndoor airEnvironmental scienceBlock (permutation group theory)Volatile organic compoundMaterials scienceEnvironmental chemistryChemistryOptoelectronicsEnvironmental engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

In addition to VOCs, 10 liquid crystal monomers (LCMs), a commercial chemical widely used in LCDs, were also observed to be released from those LCD screens. The structural identification of VOCs is based on a "building block" hypothesis (i.e., the screen-emitted VOCs originate from the "building block chemicals" used in the manufacturing of liquid crystals), which are the key components of LCD screens. The identification of LCMs is based upon the detailed information of 362 currently produced LCMs. The emission rates of VOCs and LCMs increased by up to a factor of 9, with an increase of indoor air humidity from 23 to 58% due to water-organic interactions likely facilitating the diffusion rates of organics. These findings indicate that LCD screens are a potentially important source for indoor VOCs that has not been considered previously.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

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 studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.023
GPT teacher head0.275
Teacher spread0.252 · 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 designObservational
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

Citations80
Published2021
Admission routes1
Has abstractyes

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