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Record W2725307783 · doi:10.1021/acs.chemmater.7b01797

Soluble Lead and Bismuth Chalcogenidometallates: Versatile Solders for Thermoelectric Materials

2017· article· en· W2725307783 on OpenAlexfundno aff
Hao Zhang, Jae Sung Son, Dmitriy S. Dolzhnikov, Alexander S. Filatov, Abhijit Hazarika, Yuanyuan Wang, Margaret H. Hudson, Cheng‐Jun Sun, Soma Chattopadhyay, Dmitri V. Talapin

Bibliographic record

VenueChemistry of Materials · 2017
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsnot available
FundersAir Force Office of Scientific ResearchArgonne National LaboratoryBasic Energy SciencesDivision of ChemistryDivision of Materials ResearchCanadian Light SourceNational Science Foundation
KeywordsBismuthThermoelectric materialsMaterials scienceChalcogenideThermoelectric effectSolderingNanotechnologyFabricationNanomaterialsChemical engineeringChalcogenChemistryMetallurgyComposite materialOrganic chemistryThermal conductivity

Abstract

fetched live from OpenAlex

Here we report the syntheses of largely unexplored lead and bismuth chalcogenidometallates in the solution phase. Using N 2 H 4 as the solvent, new compounds such as K 6 Pb 3 Te 6 ·7N 2 H 4 were obtained. These soluble molecular compounds underwent cation exchange processes using resin chemistry, replacing Na + or K + by decomposable N 2 H 5 + or tetraethylammonium cations. They also transformed into stoichiometric lead and bismuth chalcogenide nanomaterials with the addition of metal salts. Such a versatile chemistry led to a variety of composition-matched solders to join lead and bismuth chalcogenides and tune their charge transport properties at the grain boundaries. Solution-processed thin films composed of Bi 0.5 Sb 1.5 Te 3 microparticles soldered by (N 2 H 5 ) 6 Bi 0.5 Sb 1.5 Te 6 exhibited thermoelectric power factors (∼28 μW/cm K 2 ) comparable to those in vacuum-deposited Bi 0.5 Sb 1.5 Te 3 films. The soldering effect can also be integrated with attractive fabrication techniques for thermoelectric modules, such as screen printing, suggesting the potential of these solders in the rational design of printable and moldable thermoelectrics.

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), Insufficient payload (model declined to judge)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.266
Teacher spread0.245 · 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

Citations18
Published2017
Admission routes1
Has abstractyes

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