MétaCan
Menu
Back to cohort
Record W4313647700 · doi:10.1002/celc.202201025

Bifunctional Water Splitting Performance of NiFe LDH Improved by Pd<sup>2+</sup> Doping

2023· article· en· W4313647700 on OpenAlex

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

VenueChemElectroChem · 2023
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsMinistry of Education and Child Care
FundersNational Natural Science Foundation of China
KeywordsBifunctionalTafel equationElectrocatalystWater splittingCatalysisDopingHydroxideHydrothermal circulationTonMaterials scienceChemistryInorganic chemistryChemical engineeringElectrochemistryPhysical chemistryElectrodeOptoelectronics

Abstract

fetched live from OpenAlex

Abstract Nobel metal doping is an effective strategy to enhance the catalytic activity of electrocatalysts. Herein, a novel bifunctional electrocatalyst based on NiFe layered double hydroxide with ultra‐low Pd 2+ doping (NiFePd LDH) was constructed by a one‐step hydrothermal method, where the Pd 2+ is introduced by PdCl 4 2− . The results show that Ni 2+ and Pd 2+ species are concomitantly deposited, and the slow‐release introduction of Pd 2+ improves the element uniform distribution and effectively affects the electronic structures of active species by inducing local defects and lattice distortions, which is beneficial for stimulating the catalytic activity of NiFePd LDH. Under the optimal hydrothermal time, NiFePd LDH‐3 h only requires OER/HER overpotentials of 270 mV at 50 mA cm −2 /‐316 mV at −10 mA cm −2 , respectively, whose Tafel slopes are only 69.3/135.8 mV dec −1 . As a bifunctional catalyst, it achieves a low voltage of 1.74 V at 10 mA cm −2 for overall water splitting with excellent long‐term durability.

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 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 score0.049
Threshold uncertainty score1.000

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 studies0.0000.000
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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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