MétaCan
Menu
← Back to cohort
Record W2965790864 · doi:10.1139/cjce-2019-0215

Removal of hexavalent chromium from water using hydrochar obtained with different types of feedstock

2019· article· en· W2965790864 on OpenAlexaffvenue
Arvind V. Iyer, Erica Pensini, Ashutosh Singh

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsChemistryHexavalent chromiumChromiumRaw materialFood scienceYield (engineering)Fourier transform infrared spectroscopyNuclear chemistryMetallurgyMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Hydrochar (HC) was obtained through the hydrothermal digestion of beetroot, carrot, or spent coffee grounds. The yield (i.e., mass of HC produced per mass of feedstock used) was ≈80%, ≈20%, and ≈50% with spent coffee, carrot, and beetroot, respectively. The surface area determined with the Brunauer–Emmett–Teller method was 3.06 m2·g–1 for HC from beetroot, 1.19 m2·g–1 for HC from carrot, 2.84 m2·g–1 for HC from dark roast coffee, and 3.22 m2·g–1 for HC from original roast coffee. Fourier transform infrared spectroscopy experiments showed that amine groups had a significantly higher density in HC produced with spent coffee grounds than that produced using either beetroot or carrot. HC could sorb hexavalent chromium (Cr(VI)) from water. The concentration of Cr(VI) in water decreased from 0.3 ppm to ≈0.07 ppm and to ≈0.1 ppm within 90 min using 20 g·L–1 of HC made from spent coffee grounds and from beetroot and carrot, respectively.

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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.006
GPT teacher head0.164
Teacher spread0.159 · 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

Citations7
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Civil Engineering→Same topicAdsorption and biosorption for pollutant removal→French-language works237,207→