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Record W4400416277 · doi:10.3390/jcs8070262

Hydrolyzed Forms of Cellulose and Its Metal Composites for Hydrogen Generation: An Experimental and Theoretical Investigation

2024· article· en· W4400416277 on OpenAlexaff
Omar Faye, Inimfon A. Udoetok, Jerzy A. Szpunar, Lee D. Wilson

Bibliographic record

VenueJournal of Composites Science · 2024
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMaterials scienceMicrocrystalline celluloseMagnesiumChemical engineeringNiobiumOxideAmmonia boraneHydrogenMetalCelluloseHydrolysisHydrogen productionInorganic chemistryComposite materialHydrogen storageMetallurgyChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The quest for a smooth transition from fossil fuels to clean and sustainable energy has warranted studies on alternative energy materials. Herein, we report on an experimental and theoretical study focused on hydrogen generation through the hydrolysis of microcrystalline cellulose (MCC) treated in different media (deionized water, sodium hydroxide) and MCC functionalized with magnesium (MCC-Mg), titanium (MCC-Ti), and niobium (MCC-Nb). The XRD results reveal the decreased crystallinity of MCC due to ball milling along with the formation of metal oxide composites between MCC and various metals (magnesium, titanium, and niobium). Theoretical studies using NVT molecular dynamic simulations with the NH chain thermostat implemented in the Dmol3 provides further support to the experimental results reported herein. The results from the experimental and theoretical studies revealed that ball milling and composite formation with metal species enhanced the kinetics of the hydrolysis of MCC and, consequently, hydrogen generation, while the addition of NaOH and urea inhibited the hydrogen yield.

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 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 score0.096
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.324
Teacher spread0.294 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2024
Admission routes1
Has abstractyes

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