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Record W1978903294 · doi:10.1039/c4nr00184b

Fabrication of large-scale single-crystal bismuth telluride (Bi<sub>2</sub>Te<sub>3</sub>) nanosheet arrays by a single-step electrolysis process

2014· article· en· W1978903294 on OpenAlexaff
Hung‐Wei Tsai, Tsang‐Hsiu Wang, Tsung-Cheng Chan, Pei‐Ju Chen, Chih‐Chun Chung, Alireza Yaghoubi, Chien‐Neng Liao, Eric Wei‐Guang Diau, Yu‐Lun Chueh

Bibliographic record

VenueNanoscale · 2014
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsImpact
Fundersnot available
KeywordsNanosheetBismuth tellurideMaterials scienceFabricationSingle crystalBismuthElectrolysisTellurideNanotechnologyProcess (computing)Scale (ratio)OptoelectronicsMetallurgyCrystallographyThermoelectric materialsComputer scienceChemistryPhysical chemistryElectrodeComposite materialPhysics

Abstract

fetched live from OpenAlex

Nanolizing of thermoelectric materials is one approach to reduce the thermal conductivity and hence enhance the figure of merit. Bismuth telluride (Bi₂Te₃)-based materials have excellent figure of merit at room temperature. For device applications, precise control and rapid fabrication for the nanostructure of thermoelectric materials are essential issues. In the present study, we demonstrate a one-step electrolysis process to directly form Bi₂Te₃ nanosheet arrays (NSAs) on the surface of bulk Bi₂Te₃ with controllable spacing distance and depth by tuning the applied bias and duration. The single sheet of NSAs reveals that the average thickness and electrical resistivity of single crystalline Bi₂Te₃ in composition are 399.8 nm and 137.34 μΩ m, respectively. The formation mechanism of NSAs has been proposed. A 1.12% efficiency of quantum dot-sensitized solar cells with Bi₂Te₃ NSAs for counter electrode has been demonstrated, indicating that Bi₂Te₃ NSAs from top-down processing with a high ratio of surface area to volume are a promising candidate for possible applications such as thermoelectrics, dye-sensitized solar cells (DSSCs), and lithium-ion batteries.

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 categoriesMeta-epidemiology (narrow)
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 score0.056
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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.

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

Citations17
Published2014
Admission routes1
Has abstractyes

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