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Record W4408029636 · doi:10.1016/j.wmb.2025.02.006

Optimization of arsenic removal from water using novel renewable adsorbents derived from orange peels

2025· article· en· W4408029636 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueWaste Management Bulletin · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMemorial University of NewfoundlandMitacs
KeywordsArsenicAdsorptionOrange (colour)ChemistryRenewable energyEnvironmental scienceEnvironmental chemistryPulp and paper industryWaste managementBiologyOrganic chemistryEngineeringEcologyFood science

Abstract

fetched live from OpenAlex

• TiO 2 impacted orange peel activated carbon (ACOP-TiO 2 ) physicochemical properties. • ACOP-TiO 2 adsorption capacity for arsenic (As) reached 10.91 mg/g. • Optimal condition: pH = 4.2, As initial concentration = 50 mg/L, adsorbent dose = 3.3 g/L. • Pseudo-2nd-order kinetic and Freundlich isotherm models best describe As adsorption. • Thermodynamic studies revealed a spontaneous and endothermic process. This study developed activated carbon from orange peels (ACOP) and modified ACOP with titanium dioxide (TiO 2 ) (ACOP-TiO 2 ), focusing on optimizing the adsorption capacity of ACOP-TiO 2 for arsenic removal from water. The developed adsorbent (ACOP-TiO 2 ) was prepared and characterized by Scanning electron microscopy (SEM), Energy dispersive X-ray analysis (EDS), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), surface area analysis, and elemental analysis. The Brunauer-Emmett-Teller (BET) test demonstrated that the modification increased the surface area of ACOP-TiO 2 by 2.55 times greater than ACOP. Adsorption experiments were conducted using synthetic aqueous solutions of arsenic (As(V)), and the response surface methodology (RSM) incorporating central composite design (CCD) was employed for experimental optimization. The results indicated that ACOP-TiO 2 demonstrated efficient arsenic removal, with optimal pH identified at approximately 4.2. Increasing adsorbent dosage (0.025–0.4 g in 50 mL solution, corresponding to 0.5–8 g L -1 ) positively influenced adsorption efficiency, while initial arsenic concentration (10–60 mg L -1 ) directly correlated with adsorbent capacity, with a predicted optimum concentration of 50 mg L -1 . Contact time (0.4–6 h) exhibited minimal impact on adsorbent capacity within the experimental timeframe. Under the conditions of pH 4.2, an initial arsenic concentration of 50 mg L -1 , an adsorbent dose of 3.3 g L -1 (0.165 g adsorbent/50 mL solution), and a contact time of 4.8 h, the maximum adsorbent capacity in arsenic removal for ACOP-TiO 2 was 10.91 mg g −1 . The intra-particle diffusion kinetic model and Temkin isotherm best described arsenic adsorption onto ACOP-TiO 2 . This research contributes valuable insights into utilizing agricultural waste for water treatment, offering a sustainable and economical solution for arsenic removal.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.011
GPT teacher head0.210
Teacher spread0.199 · 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