Evidence map›Paper›PMID 40283268›Full record

ArticleMicromachines2025

Lasso Model-Based Optimization of CNC/CNF/rGO Nanocomposites.

Ghazaleh Ramezani, Ixchel Ocampo Silva, Ion Stiharu, Theo G M van de Ven, Vahe Nerguizian

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Micromachines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ghazaleh RamezaniDepartment of Mechanical and Industrial Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.ORCID 0009-0006-8797-9136
Ixchel Ocampo SilvaSchool of Engineering and Sciences, Tecnológico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey 64849, Mexico.ORCID 0000-0002-7486-4616
Ion StiharuDepartment of Mechanical and Industrial Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.ORCID 0000-0001-7505-1441
Theo G M van de VenDepartment of Chemistry, McGill University, Montreal, QC H4A 3J1, Canada.
Vahe NerguizianDépartement de Génie Électrique, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study explores the use of citric acid and L-ascorbic acid as reducing agents in CNC/CNF/rGO nanocomposite fabrication, focusing on their effects on electrical conductivity and mechanical properties. Through comprehensive analysis, L-ascorbic acid showed superior reduction efficiency, producing rGO with enhanced electrical conductivity up to 2.5 S/m, while citric acid offered better CNC and CNF dispersion, leading to higher mechanical stability. The research employs an advanced optimization framework, integrating regression models and a neural network with 30 hidden layers, to provide insights into composition-property relationships and enable precise material tailoring. The neural network model, trained on various input variables, demonstrated excellent predictive performance, with R

Indexed as

citric acidCNC/CNF/rGO nanocompositeselectrical conductivitygraphene oxide reductionL-ascorbic acidmulti-objective optimizationregression modelingtensile strength

Identifiers

PMID40283268
PMCPMC12029604

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.