Evidence map›Paper›PMID 39170263›Full record

ReviewHeliyon2024

Coconut (Cocos nucifera) sheath-based polymeric composites - A review.

Tabrej Khan, N Karthikeyan, Jesuarockiam Naveen, Suya Prem Anand, Tamer A Sebaey

Abstract readReview
In one paragraph

Review in Heliyon, 2024. 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.

Tabrej KhanDepartment of Engineering Management, Faculty of Engineering, Prince Sultan University, Riyadh, 11586, Saudi Arabia.
N KarthikeyanSchool of Mechanical Engineering, Vellore Institute of Technology, Vellore, 632014, Tamilnadu, India.
Jesuarockiam NaveenSchool of Mechanical Engineering, Vellore Institute of Technology, Vellore, 632014, Tamilnadu, India.
Suya Prem AnandSchool of Mechanical Engineering, Vellore Institute of Technology, Vellore, 632014, Tamilnadu, India.
Tamer A SebaeyDepartment of Engineering Management, Faculty of Engineering, Prince Sultan University, Riyadh, 11586, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ever-burgeoning sustainable need for humanity to produce lighter, tougher, and more cost-effective materials has led to the development of biodegradable composites. Ever since their creation, natural fiber-based composites have found themselves ubiquitous. Due to their exceptional performance, Natural fiber-reinforced composites have been predominantly used in several engineering applications. Coconut leaf sheath (CLS) is an abundantly available agro-waste that can be easily extracted from the coconut tree. This review investigates the potential of incorporating coconut sheath into polymeric matrices. Also, the effects of surface treatments, synthetic fiber hybridization, and nanofiller-modified matrices were analyzed in detail. It has been observed that surface modification of coconut sheath, hybridization with other natural or synthetic fibers, and nanofiller-modified polymeric composites exhibit better mechanical performance compared to monolithic coconut sheath-based polymeric composites. One of the key advantages of hybrid composites is that they can combine the strengths of different constituents to overcome their individual limitations. Moreover, coconut sheath-based hybrid composites enhance the composites' damage tolerance and reduce the material cost.

Indexed as

And nanofillersBiocompositesChemical treatmentHybrid compositeNatural CLSStacking sequence

Identifiers

PMID39170263
PMCPMC11337133

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.