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Record W4224922573 · doi:10.1039/d2nr01135b

Doped 2D SnS materials derived from liquid metal-solution for tunable optoelectronic devices

2022· article· en· W4224922573 on OpenAlexfundno aff
Xiangyang Guo, Yichao Wang, Aaron Elbourne, Aishani Mazumder, Chung Kim Nguyen, Vaishnavi Krishnamurthi, J. Yu, Peter C. Sherrell, Torben Daeneke, Sumeet Walia, Yongxiang Li, Ali Zavabeti

Bibliographic record

VenueNanoscale · 2022
Typearticle
Languageen
FieldEngineering
TopicChalcogenide Semiconductor Thin Films
Canadian institutionsnot available
FundersUniversity of MelbourneRMIT UniversityAustralian Research CouncilOntario Ministry of Natural Resources and Forestry
KeywordsMaterials scienceDopingOptoelectronicsMetalNanotechnologyMetallurgy

Abstract

fetched live from OpenAlex

piezoelectric coefficient. In addition, photodetector response times have increased by several orders of magnitude. Doping methods using liquid metals have significantly changed the photodiode and piezoelectric device performances, providing a powerful approach to tune optoelectronic device outputs.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.017
GPT teacher head0.228
Teacher spread0.212 · 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 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

Citations23
Published2022
Admission routes1
Has abstractyes

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