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Record W4412545336 · doi:10.1021/acsami.5c10046

Effect of Barrier Material Accumulation on the Performance of Bi<sub>2</sub>Te<sub>3</sub>-Based Thermoelectric Generators

2025· article· en· W4412545336 on OpenAlexafffund
Kun Song, Junhua Xie, Guosong Liu, Wei Zhao, Shuang Wang, Junhao Meng, Luqiao Qi, Jie Zheng, Peter Schiavone

Bibliographic record

VenueACS Applied Materials & Interfaces · 2025
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsMaterials scienceThermoelectric effectThermoelectric materialsEngineering physicsThermoelectric generatorNanotechnologyOptoelectronicsComposite materialThermal conductivityThermodynamics

Abstract

fetched live from OpenAlex

The barrier material placed between thermoelectric (TE) materials and electrodes plays a dominant role in the performance of a thermoelectric generator (TEG). Nickel (Ni) is widely used as a barrier material in Bi 2 Te 3 -based TEGs. Since the electroplating of Ni is an efficient technique in the coating of barrier materials, we observe that the barrier material accumulation on the surface of TE materials greatly restricts the output power and conversion efficiency of TEGs. Specifically, for an interface with accumulated barrier material, the output power can be reduced by more than 30%, while the interfacial strength can be reduced by around 18%. By optimizing the electroplating process, we realize a maximum output power of 0.62 W and a maximum conversion efficiency of 5.1% in a Bi 2 Te 3 -based TEG, which is much higher than that of commercial TEGs. Simultaneously, the bonding strength of the uniform interface reaches 49.36 N, which is 22.6% higher than that of the interface with barrier material accumulation. These results guide the design and fabrication of high-performance TEGs.

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 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 score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.008
GPT teacher head0.244
Teacher spread0.237 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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