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Record W4224248883 · doi:10.1126/sciadv.abm6541

Nanoalloy libraries from laser-induced thermionic emission reduction

2022· article· en· W4224248883 on OpenAlex
Haoqing Jiang, Xingtao Liu, Mengnan Zhu, Jin Xu, Licong An, Peng‐Fei Sui, Jing‐Li Luo, Gary J. Cheng

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldEngineering
TopicLaser-Ablation Synthesis of Nanoparticles
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceThermionic emissionNanometreLaserNanotechnologyMetalNanoparticleFabricationCarbon fibersChemical engineeringCatalysisNanosecondChemistryMetallurgyComposite materialComposite numberOrganic chemistryOptics

Abstract

fetched live from OpenAlex

Nanoalloys, especially high-entropy nanoalloys (HENAs) that contain equal stoichiometric metallic elements in each nanoparticle, are widely used in vast applications. Currently, the synthesis of HENAs is challenged by slow reaction kinetics that leads to phase segregation, sophisticated pretreatment of precursors, and inert conditions that preclude scalable fabrication of HENAs. Here, we report direct conversion of metal salts to ultrafine HENAs on carbonaceous support by nanosecond pulsed laser under atmospheric conditions. Because of the unique laser-induced thermionic emission and etch on carbon, the reduced metal elements were gathered to ultrafine HENAs and stabilized by defective carbon support. This scalable, facile, and low-cost method overcomes the immiscible issue and can produce various HENAs uniformly with a size of 1 to 3 nanometers and metal elements up to 11 with productivity up to 7 grams per hour. One of the senary HENAs exhibited excellent catalytic performance in oxygen reduction reaction, manifesting great potential in practical applications.

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.

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 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 score0.036
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
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 head0.015
GPT teacher head0.234
Teacher spread0.219 · 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