MétaCan
Menu
Back to cohort
Record W3108257971 · doi:10.1002/adfm.202008452

Sec‐Eliminating the SARS‐CoV‐2 by AlGaN Based High Power Deep Ultraviolet Light Source

2020· article· en· W3108257971 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced Functional Materials · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsMinistry of Education and Child Care
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceUltravioletSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)OptoelectronicsPower (physics)Ultraviolet aLight sourceUltraviolet light2019-20 coronavirus outbreakVirologyOpticsBiologyPhysics

Abstract

fetched live from OpenAlex

Abstract The world‐wide spreading of coronavirus disease (COVID‐19) has greatly shaken human society, thus effective and fast‐speed methods of non‐daily‐life‐disturbance sterilization have become extremely significant. In this work, by fully benefitting from high‐quality AlN template (with threading dislocation density as low as ≈6×10 8 cm −2 ) as well as outstanding deep ultraviolet (UVC‐less than 280 nm) light‐emitting diodes (LEDs) structure design and epitaxy optimization, high power UVC LEDs and ultra‐high‐power sterilization irradiation source are achieved. Moreover, for the first time, a result in which a fast and complete elimination of SARS‐CoV‐2 (the virus causes COVID‐19) within only 1 s is achieved by the nearly whole industry‐chain‐covered product. These results advance the promising potential in UVC‐LED disinfection particularly in the shadow of COVID‐19.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.034
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.217
Teacher spread0.207 · 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