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Record W2966173589 · doi:10.5539/jsd.v12n4p140

Activated Carbon Obtained from Coffee and Orange Wastes

2019· article· en· W2966173589 on OpenAlexvenueno aff
Dorian M. Godinez-Adame, Job Alí Díaz-Hernández, Luis E. Alvarez-Jacinto, Ludwig I.C. Ortiz-Garcia, Emily G. Cahum-Chan, Sheila M. Canul-Petul, Claudia B. Santiago-Martinez, Lourdes J. Solis-Uc, Jessica Borbolla‐Vázquez

Bibliographic record

VenueJournal of Sustainable Development · 2019
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsnot available
Fundersnot available
KeywordsActivated carbonOrange (colour)ChemistryPyrolysisAbsorption capacityAdsorptionPulp and paper industryOrganic chemistryFood scienceChemical engineering

Abstract

fetched live from OpenAlex

Organic coffee and orange wastes have increased considerably in the last decade. In order to utilize this "garbage", the present study focuses on the obtaining of activated carbon from them. The pyrolysis of the samples, followed by chemical activation and subsequent neutralization, allowed the establishment of two protocols “A” and “B”, with slight variations depending on the residue. The results indicate that the efficiency of the activated carbon from coffee grounds using protocol "A" and "B" was 3.68% and 6.30%, respectively. On the other hand, the carbon obtained from orange peels had an efficiency of 5.00 % and 2.88 %, respectively. To confirm that the activated carbon from each type of waste has adsorption and absorption capacity, we performed a colorimetric analysis with methyl blue. These analyses showed that the activated carbon from coffee grounds and orange peels have a retention capacity of 91.09 and 95.25%, respectively, while the retention capacity of a commercial activated carbon was 99.23 %. In this preliminary study, it is shown that several residues considered "garbage" can be used sustainably.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.401

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)0.0000.000

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 head0.012
GPT teacher head0.257
Teacher spread0.245 · 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 designObservational
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

Citations2
Published2019
Admission routes1
Has abstractyes

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