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Record W4294276201 · doi:10.1038/s41598-022-18883-5

Photo-engineered optoelectronic properties of indium tin oxide via reactive laser annealing

2022· article· en· W4294276201 on OpenAlexfundno aff
James Hillier, P. Patsalas, Dimitrios Karfaridis, S. Camelio, W. M. Cranton, Alexei Nabok, Christopher J. Mellor, Demosthenes C. Koutsogeorgis, N. Kalfagiannis

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsnot available
FundersEuropean Regional Development FundUniversity of IoanninaTrent UniversityNottingham Trent University
KeywordsAnnealing (glass)Materials scienceIndium tin oxideOptoelectronicsTinIndiumLaserNanotechnologyMetallurgyThin filmOpticsPhysics

Abstract

fetched live from OpenAlex

Transparent conductive oxides are appealing materials for optoelectronic and plasmonic applications as, amongst other advantages, their properties can be modulated by engineering their defects. Optimisation of this adjustment is, however, a complex design problem. This work examined the modification of the carrier transport properties of sputtered tin-doped indium oxide (ITO) via laser annealing in reactive environments. We relate the optical modifications to the structural, compositional, and electronic properties to elucidate the precise mechanisms behind the reactive laser annealing (ReLA) process. For sufficiently high laser fluence, we reveal an ambient-dependent and purely compositional modulation of the carrier concentration of ITO thin films. Hereby, we demonstrate that ReLA utilises the precise energy delivery of photonic processing to enhance the carrier mobility and finely tune the carrier concentration without significantly affecting the crystal structure. Exploitation of this phenomena may enable one to selectively engineer the optoelectronic properties of ITO, promising an alternative to the exploration of new materials for optoelectronic and photonic applications.

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)0.002
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 score0.010
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.212
Teacher spread0.196 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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