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Record W1819706261 · doi:10.5539/jmsr.v5n1p24

Preparation of Activated Carbon from Desiccated Coconut Residue by Chemical Activation with NaOH

2015· article· en· W1819706261 on OpenAlexvenueno aff
Mohd Adib Yahya, Che Wan Zanariah Che Wan Ngah, M. A. Hashim, Zakaria Al‐Qodah

Bibliographic record

VenueJournal of Materials Science Research · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNatural Products and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsCarbonizationSodium hydroxideMaterials scienceActivated carbonNitrogenResidue (chemistry)Nuclear chemistrySpecific surface areaSodiumChemical engineeringComposite materialAdsorptionOrganic chemistryChemistryScanning electron microscopeMetallurgyCatalysis

Abstract

fetched live from OpenAlex

This study investigates the effect of temperature and impregnation ratio on the physicochemical properties of activated carbon prepared from agricultural waste; desiccated coconut residue (DCR) by chemical activation using sodium hydroxide (NaOH). DCR sample was first carbonized at three different temperatures for 1 hour at 400°C, 500°C, and 600°C respectively. The resulting chars were impregnated with NaOH at three different impregnation ratio; 1:1, 1:2, and 1:3 respectively and activated under nitrogen atmosphere for 1 hour at three different temperatures based on its carbonization temperature. The specific surface area was strongly affected by impregnation ratio in which increased with impregnation ratio. The specific surface area also increased with temperature but then decreased at highest desired temperature.

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 score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.086
GPT teacher head0.370
Teacher spread0.284 · 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

Citations35
Published2015
Admission routes1
Has abstractyes

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