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Record W4401088839 · doi:10.18280/acsm.480305

Preparation and Characterization of Water Hyacinth Stems (Eichhornia Crassipes) Impregnated with Modified Polystyrene

2024· article· en· W4401088839 on OpenAlexvenueno aff

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicConstructed Wetlands for Wastewater Treatment
Canadian institutionsnot available
FundersUniversitas Negeri Medan
KeywordsHyacinthEichhornia crassipesPolystyreneCharacterization (materials science)BotanyChemistryBiologyAquatic plantMaterials scienceOrganic chemistryNanotechnologyEcology

Abstract

fetched live from OpenAlex

Stems of water hyacinth contain cellulose, a natural polymer capable of interacting with modified polystyrene.This research aims to determine the physical and mechanical properties of water hyacinth stems and to trigger the modification of polystyrene with acrylic acid using the initiator benzoyl peroxide.The impregnation technique is used to modify water hyacinth stems with modified polystyrene, resulting in optimum conditions at an impregnation time of 3 h.The results of impregnation reveal a decrease in water content from 17.97% to 6.45%, a decrease in water absorption from 94.85% to 81.37%, and an increase in the modulus of elasticity (MoE) from 22.94 MPa to 191.18 MPa.Then FT-IR analysis was carried out to determine the success of the polystyrene modification, and the surface structure of the impregnated water hyacinth stems was examined using SEM.Therefore, from these data, it can be seen that modified polystyrene resin can improve the mechanical and physical properties of water hyacinth stems.

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 score0.002

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 head0.011
GPT teacher head0.237
Teacher spread0.226 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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