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Record W2905943557 · doi:10.24102/ijes.v7i2.909

Formation of Hierarchical Zinc Oxide Nanostructures for Solar Energy Converters and Photovoltaics

2018· article· en· W2905943557 on OpenAlexvenueno aff
Lavrynenko S.N.

Bibliographic record

VenueInternational Journal of Environment and Sustainability · 2018
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsnot available
Fundersnot available
KeywordsPhotovoltaicsZincMaterials scienceConvertersNanostructurePhotovoltaic systemNanotechnologySolar energySolar energy conversionEngineering physicsMetallurgyEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Zinc oxide is a promising material for the fabrication of thin film layers for a new generation of photovoltaic devices and solar thermal collectors, due to the unique optical and electrical properties as well as the propensity to form fil­amentous one-dimensional submicron structures, namely nanowires, nanorods, nanotubes, nanobelts, and hierarchical nanostructures with developed surface and projected superhydrophobicity. We identified the possibility for a develop­ment of planar single-layer antireflection coatings and/or arrays of nanorods of this material as having the shape of hexagonal prisms, and demonstrating the moth eye effect on the substrates of transparent conductive tin dioxide and on silicon wafers with embedded homojunctions. The optimization of the pulsed electrodeposition of zinc oxide arrays adjust the size of parabolic nanonipples for the implementation of antireflection coatings with the moth eye effect on var­ious substrates, including flexible. The antireflection coatings will be developed for thin-film photovoltaic devices of substrate configuration based on kesterite, tin sulphide, and fullerene layers, for the cadmium telluride based photovoltaic cells with bilateral sensitivity of superstrate configuration on the flexible sub­strates and for optoelectronic devices based on zinc selenide for the ultraviolet spectra.

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)0.000
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 score0.060
Threshold uncertainty score0.194

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)0.0000.000

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 head0.008
GPT teacher head0.233
Teacher spread0.225 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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