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Record W4297223563 · doi:10.3390/nano12193328

Stainless Steel-Supported Amorphous Nickel Phosphide/Nickel as an Electrocatalyst for Hydrogen Evolution Reaction

2022· article· en· W4297223563 on OpenAlexaff
Gaoyang Liu, Faguo Hou, Xindong Wang, Baizeng Fang

Bibliographic record

VenueNanomaterials · 2022
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of British Columbia
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsPhosphideMaterials scienceAmorphous solidTafel equationElectrocatalystNickelOverpotentialChemical engineeringElectrochemistryMetallurgyCatalysisElectrolysis of waterElectrolysisElectrodeCrystallographyChemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Recently, nickel phosphides (Ni-P) in an amorphous state have emerged as potential catalysts with high intrinsic activity and excellent electrochemical stability for hydrogen evolution reactions (HER). However, it still lacks a good strategy to prepare amorphous Ni-P with rich surface defects or structural boundaries, and it is also hard to construct a porous Ni-P layer with favorable electron transport and gas-liquid transport. Herein, an integrated porous electrode consisting of amorphous Ni-P and a Ni interlayer was successfully constructed on a 316L stainless steel felt (denoted as Ni-P/Ni-316L). The results demonstrated that the pH of the plating solution significantly affected the grain size, pore size and distribution, and roughness of the cell-like particle surface of the amorphous Ni-P layer. The Ni-P/Ni-316L prepared at pH = 3 presented the richest surface defects or structural boundaries as well as porous structure. As expected, the as-developed Ni-P/Ni-316L demonstrated the best kinetics, with η10 of 73 mV and a Tafel slope of ca. 52 mV dec-1 for the HER among all the electrocatalysts prepared at various pH values. Furthermore, the Ni-P/Ni-316L exhibited comparable electrocatalytic HER performance and better durability than the commercial Pt (20 wt%)/C in a real water electrolysis cell, indicating that the non-precious metal-based Ni-P/Ni-316L is promising for large-scale processing and practical use.

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)
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 score0.051
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.010
GPT teacher head0.238
Teacher spread0.228 · 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

Citations12
Published2022
Admission routes1
Has abstractyes

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