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Record W4225163145 · doi:10.11159/iceptp22.196

Food Wastes As Adsorbent Materials for Water Decontamination: The Use of Kiwi Peels To Remove Emerging Pollutants and Textile Dyes

2022· article· en· W4225163145 on OpenAlexvenueno aff
Vito Rizzi, Jennifer Gubitosa, Paola Fini, Angela Agostiano, Pinalysa Cosma

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2022
Typearticle
Languageen
FieldChemistry
TopicDye analysis and toxicity
Canadian institutionsnot available
Fundersnot available
KeywordsKiwiWaste managementHuman decontaminationPollutantCircular economyEnvironmental scienceTextileAgricultureContaminationWastewaterPulp and paper industryChemistryEnvironmental engineeringEngineeringMaterials scienceFood science

Abstract

fetched live from OpenAlex

The high rate of resource consumption and large amounts of produced wastes have been reported to drive towards an ecological collapse. Interestingly, a circular economy approach could reduce this environmental concern avoiding the waste management, and all outputs (products, by-products, wastes) would become inputs (material and energy) to other processes. When the model is based on the production of renewable biological resources, and these resources are converted into value added products, the concept of bio-circular economy take place. [1,2] It means to develop an economy plan based on the production from biological resources with a sustainable transformation of wastes, offering alternatives to their dumping, burning, composting etc. About this purpose, the use of fruit Peels as food/agricultural wastes have attained interest, as adsorbent materials for water purification, avoiding their disposal according to the principles of Green Chemistry and Sustainable Development.[3] For this purpose, this work proposes, among wastes, the use of Kiwi Peels to remove emerging pollutants (not regulated substances that could affect both human health, and the whole environment, causing severe problems [1,2]) and textile dyes from water. Indeed, among the explored wastes, Kiwi Peels removed the largest number of contaminants. Kiwi Peels were characterized by adopting in synergy FTIR-ATR, TG and SEM analyses, before and after their use, and as result they are proposed as recyclable adsorbent. To infer information about the behaviour of Kiwi Peels during water treatments, model contaminants were selected and investigated (Ciprofloxacin, CIP, and Direct Blue 78, DB); so, the role of several parameters affecting the process was assessed. The thermodynamic, the adsorption isotherms and kinetics were also studied. Finally, to extend the lifetime of Kiwi Peels, desorption experiments were carried out by using hot water or salt solutions. 10 cycles of adsorption/desorption were studied, evidencing the recycling of both pollutants and Kiwi Peels (Figure 1). Moreover, another aspect investigated in this work regards the possibility of using Advance Oxidation Processes (AOPs) to induce the pollutants solid-state photodegradation as an alternative approach for adsorbent regeneration. Also, in this case, FTIR-ATR, SEM, and TG analyses were used in synergy for investigating the adsorbent features after the AOPs’ application. If, on the one hand, the SEM and FTIR-ATR results revealed the absence of important post-treatment changes, on the other hand, the TGA suggested some modifications. Finally, mixtures of pollutants were also studied and in the case of dyes, dyeing experiments were also performed, evidencing the dye ability to colour cotton fibers after the colour recycling (Figure 2). Particularly, the experiments of dyeing were executed during the Kiwi Peels desorption in hot water at 323 K, without further additives

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.009
GPT teacher head0.196
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), 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
Published2022
Admission routes1
Has abstractyes

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