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Record W4414440948 · doi:10.1016/j.jobab.2025.09.003

Evaluating nanocellulose from food waste as a functional amendment for sandy soils: Linking fiber structure to water dynamics, soil mechanics, and plant-microbes interactions

2025· article· en· W4414440948 on OpenAlexvenueno aff
M-Haidar Ali Dali, Mohamed Hamid Salim, Malak AbuZaid, Maryam O. Qassem, Faisal Al Marzooqi, Andrea Ceriani, Alessandro Decarlis, Ludovic F. Dumée, Blaise L. Tardy

Bibliographic record

VenueJournal of Bioresources and Bioproducts · 2025
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsnot available
FundersKhalifa University of Science, Technology and Research
KeywordsAmendmentSoil waterNanocelluloseFiberWater retentionCelluloseBiomass (ecology)Nanofiber

Abstract

fetched live from OpenAlex

Micro and nanofibers have the ability to imbue control over water transport properties and mechanical cohesion to granular materials. These key characteristics are proportional to the fiber size, if finely tuned, and can enable soils to more effectively host life. Typically, requirements include a high organic matter content, a rich microbiome, and especially physico-chemical properties conducive to water dynamics. Herein, we developed mechanochemical processes to fibrillate food-waste-based biomass (namely, peels) into a range of fiber solutions. Macrofibers and nanofibers were obtained via mild processing steps and were fully characterized, the relation between the morphology as well as physico-chemical properties of the fibers was thoroughly studied. Three sand types associated with deserts were evaluated for their potential benefits from the fiber amendments. The compressive response of the amended soils and, more importantly, their water holding, water permeability, and evaporation rate were thoroughly evaluated. The resistance of reinforced soil matrices to biodegradation and dry-wet cycling was also used to evaluate long-term performance. Finally, this study provides an outlook on nutrient retention for agricultural endeavors as a function of fiber amendment type and content.

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 score0.009
Threshold uncertainty score0.515

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 studies0.0000.000
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 head0.027
GPT teacher head0.296
Teacher spread0.269 · 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

Citations6
Published2025
Admission routes1
Has abstractyes

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