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Record W2955032676 · doi:10.1002/aenm.201901244

Highly Passivated n‐Type Colloidal Quantum Dots for Solution‐Processed Thermoelectric Generators with Large Output Voltage

2019· article· en· W2955032676 on OpenAlexaff
Mohamad Insan Nugraha, Hyunho Kim, Bin Sun, Saheena Desai, F. Pelayo Garcı́a de Arquer, Edward H. Sargent, Husam N. Alshareef, Derya Baran

Bibliographic record

VenueAdvanced Energy Materials · 2019
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Thermoelectric Materials and Devices
Canadian institutionsUniversity of Toronto
FundersKing Abdullah University of Science and Technology
KeywordsMaterials scienceThermoelectric effectThermoelectric generatorChalcogenidePassivationQuantum dotOptoelectronicsSeebeck coefficientThermoelectric materialsNanotechnologyThermal conductivityVoltageElectrical engineeringComposite material

Abstract

fetched live from OpenAlex

Abstract Colloidal quantum dots (CQDs) are attractive materials for thermoelectric applications due to their simple and low‐cost processing; advantageously, they also offer low thermal conductivity and high Seebeck coefficient. To date, the majority of CQD thermoelectric films reported upon have been p‐type, while only a few reports are available on n‐type films. High‐performing n‐ and p‐type films are essential for thermoelectric generators (TEGs) with large output voltage and power. Here, high‐thermoelectric‐performance n‐type CQD films are reported and showcased in high‐performance all‐CQD TEGs. By engineering the electronic coupling in the films, a thorough removal of insulating ligands is achieved and this is combined with excellent surface trap passivation. This enables a high thermoelectric power factor of 24 µW m −1 K −2 , superior to previously reported n‐type lead chalcogenide CQD films operating near room temperature (<1 µW m −1 K −2 ). As a result, an all‐CQD film TEG with a large output voltage of 0.25 V and a power density of 0.63 W m −2 at ∆ T = 50 K is demonstrated, which represents an over fourfold enhancement to previously reported p‐type only CQD 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)0.000
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 score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.221
Teacher spread0.215 · 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
Published2019
Admission routes1
Has abstractyes

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