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Record W4389623988 · doi:10.3390/coatings13122069

Effect of NaF Doping on the Microstructure and Thermoelectric Performance of BiCuSeO Ceramics

2023· article· en· W4389623988 on OpenAlexaff
Zhenbing Pei, Guangqi Xiang, Xin Sun, Zhuang Fu, Yanxin Qiao, Lei Wang, Songtao Dong, Jian Chen

Bibliographic record

VenueCoatings · 2023
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsWestern University
FundersNanjing University
KeywordsMaterials scienceThermoelectric effectCeramicMicrostructureSeebeck coefficientThermal conductivityDopingElectrical resistivity and conductivityPower factorBall millComposite materialThermoelectric materialsOptoelectronicsElectrical engineeringPower (physics)Thermodynamics

Abstract

fetched live from OpenAlex

The layered oxyselenide BiCuSeO has attracted significant attention due to its ability to demonstrate low thermal conductivity and a high Seebeck coefficient. This research project involved the synthesis of Bi1−xNaxCuSeO1−xFx (x = 0, 0.05, 0.10, 0.15, and 0.20) ceramics using high-energy ball milling and cold isostatic pressing techniques. A comprehensive investigation was conducted to examine the influence of co−doping NaF on the thermoelectric properties of BiCuSeO ceramics. The substitution of Bi3+ with Na+ introduces a substantial number of holes, resulting in a remarkable improvement in the electrical conductivity and power factor. The conductivity was significantly increased from 9.10 S cm−1 (BiCuSeO) to 94.5 S cm−1 (Bi0.85Na0.15CuSeO0.85F0.15) at 323 K. Additionally, at 823 K, the power factor of the Bi0.85Na0.15CuSeO0.85F0.15 sample reached 44.8 × 10−5 W/m K2. Furthermore, the Bi1−xNaxCuSeO1−xFx ceramics demonstrated a minimum thermal conductivity of 0.43 W m−1 K−1. Consequently, the Bi0.85Na0.15CuSeO0.85F0.15 sample achieved a maximum ZT value of 0.78, which is 7.09 times higher than that of the pure BiCuSeO sample (0.11).

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 score0.003
Threshold uncertainty score0.260

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 studies0.0000.000
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 head0.006
GPT teacher head0.234
Teacher spread0.227 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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