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Record W4295868309 · doi:10.14447/jnmes.v25i3.a08

Carboxymethyl Tamarind Kernel Gum /ZnO- Biocomposite: As an Antifungal and Hazardous Metal Removal Agent

2022· article· en· W4295868309 on OpenAlexvenueno aff
Jagram Meena, Harish Chandra, Sudhir G. Warkar

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2022
Typearticle
Languageen
FieldMedicine
TopicMedicinal Plants and Neuroprotection
Canadian institutionsnot available
FundersDelhi Technological University
KeywordsNanocompositeMaterials scienceFourier transform infrared spectroscopyBiocompositeNuclear chemistryTransmission electron microscopyScanning electron microscopeAdsorptionBiopolymerHigh-resolution transmission electron microscopyCarboxymethyl celluloseChemical engineeringComposite materialNanotechnologyChemistryMetallurgyOrganic chemistryPolymerComposite number

Abstract

fetched live from OpenAlex

ZnO nanoparticles (ZnO NPs) were in situ mixed with carboxymethyl tamarind kernel gum to generate the new biocomposite. High-resolution transmission electron microscopy (HR-TEM), field emission scanning electron microscopy (FE-SEM), Fourier transform infrared (FTIR), x-ray diffraction analysis (XRD), and dynamic light scattering (DLS)were used to characterize the CMTKG/ZnO nanocomposites. Numerous characterizations were utilized to prove that ZnO NPs had been integrated into the biopolymer matrix. The standard size of the CMTKG/ZnO nanocomposites was developed to be greater than 32–40 nm using high-resolution transmission electron microscopy and x-ray analysis de-Scherer methods. Chromium (VI) was removed from the aqueous solution using the nanocomposite (CMTKG/ZnO) as an adsorbent. The nanocomposite reached its maximum adsorption during 80 minutes of contact time, 30 mg/L chromium (VI) concentration, 2.0 g/L adsorbent part, and 7.0 pH. Further research into the antifungal activity of CMTKG/ZnO nanocomposites against Aspergillus flavus MTCC-2799 was conducted.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.019
GPT teacher head0.277
Teacher spread0.257 · 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 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

Citations13
Published2022
Admission routes1
Has abstractyes

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