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Record W4414245687 · doi:10.1021/acs.nanolett.5c04048

Enhanced Reaction Kinetics of Highly Selective Oxidation of 5-Hydroxymethylfurfural to 2,5-Furandicarboxylic Acid over NiCo<sub>2</sub>S<sub>4</sub>

2025· article· en· W4414245687 on OpenAlexaff
Linlin Wang, Lin Chen, Ruirui Xu, Mohd Adnan Khan, Jun Zhao, Xinti Yu, Jonas Björk, Johanna Rosén, Jinguang Hu, Zhangxin Chen, Heng Zhao

Bibliographic record

VenueNano Letters · 2025
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersNingbo Municipal People's GovernmentGöran Gustafssons Stiftelse för Naturvetenskaplig och Medicinsk Forskning
KeywordsAnodeFaraday efficiencyCatalysisYield (engineering)KineticsRenewable energyExergonic reaction

Abstract

fetched live from OpenAlex

Selective valorization of biomass feedstocks and their derivatives, using renewable electricity to produce value-added chemicals, is a promising route to decarbonize the chemical industry. Herein, we demonstrate the selective production of 2,5-furandicarboxylic acid (FDCA) from the electrocatalytic oxidation of 5-hydroxymethylfurfural (HMF). NiCo 2 S 4 as an anode electrode exhibits almost 100% yield of FDCA with >90% Faradaic efficiency (FE) and high stability at optimized conditions, which outperforms pure NiS x and CoS x . Theoretical calculations reveal that the enhanced reaction kinetics for HMF conversion to FDCA on NiCo 2 S 4 arises from an optimal OH coverage and a more exergonic rate-limiting step compared with its monometallic counterparts. A techno-economic analysis (TEA) for a 100-ton/day FDCA industrial scale production facility reveals that the NiCo 2 S 4 catalyst could drive economic feasibility of the process. This work provides an economically feasible approach to sustainably produce FDCA from HMF oxidation by electrocatalysis.

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)
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 score0.013
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
Bibliometrics0.0010.001
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 head0.003
GPT teacher head0.194
Teacher spread0.191 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

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