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Record W4283588153 · doi:10.11159/tann22.139

Formation of NiO Thin-Film via Picosecond Laser Pulses for Energy Storage Electrode Fabrication

2022· article· en· W4283588153 on OpenAlexafffund
Mayuresh Khot, Amirkianoosh Kiani

Bibliographic record

VenueProceedings of the International Conference of Theoretical and Applied Nanoscience and Nanotechnology · 2022
Typearticle
Languageen
FieldMaterials Science
TopicTransition Metal Oxide Nanomaterials
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNon-blocking I/OMaterials scienceFabricationPicosecondOptoelectronicsElectrodeEnergy storageLaserThin filmOpticsNanotechnologyChemistryPower (physics)Physics

Abstract

fetched live from OpenAlex

Consumer demand in portable and mobile electronics has noticed exponential rise leading to increases demand for energy storage devices. Once such device is supercapacitor which relies on interfacial, surface charge storage. With increased surface area, better capacitance is observed with assistance of nanoengineering. In this paper, a thin oxide layer is generated on Ni sheet surface with ultra-short pulses for laser ablation of Ni. The effects of power were analysed by keeping other parameters such as pulse duration, frequency, and irradiation scan speed constant. The 3D nanostructures of metal oxide exhibited pseudocapacitive behaviour. The nanostructured oxide layer assisted in better ion diffusion and ion adsorption and desorption as observed by the electrochemical tests performed. The paper promotes synthesis of nanoparticles (NPs) with green approach such as laser ablation as part of future manufacturing methods for electrode fabrication

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)0.001
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 score0.259
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.214
Teacher spread0.204 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the International Conference of Theoretical and Applied Nanoscience and NanotechnologySame topicTransition Metal Oxide NanomaterialsFrench-language works237,207