Evidence map›Paper›PMID 34451334›Full record

ArticlePolymers2021

Preparation and Characterization of Eco-Friendly Spent Coffee/ENR50 Biocomposite in Comparison to Carbon Black.

Gunasunderi Raju, Mohammad Khalid, Mahmoud M Shaban, Baharin Azahari

Open access · goldAbstract read
In one paragraph

Article in Polymers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.4field-weighted citation impact, top 21% of its field
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

2 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
  2. Review
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

4 authors at 3 institutions in 2 countries.

Gunasunderi RajuSchool of Distance Education, Universiti Sains Malaysia, 11800 Penang, Malaysia.
Mohammad KhalidGraphene & Advanced 2D Materials Research Group (GAMRG), School of Engineering and Technology, Sunway University, 47500 Petaling Jaya, Selangor, Malaysia.ORCID 0000-0002-0265-4820
Mahmoud M ShabanEgyptian Petroleum Research Institute, Nasr City, Cairo 11727, Egypt.ORCID 0000-0002-7144-0316
Baharin AzahariSchool of Industrial Technology, Universiti Sains Malaysia, 11800 Penang, Malaysia.
Universiti Sains Malaysia · MYEgyptian Petroleum Research Institute · EGSunway University · MY

Funding

Universiti Sains Malaysia Research University Incentive (RUI) [Grant number 1001/PJJAUH/ 8011055].
6 · The paper itself

Abstract

This study investigates the impact of spent coffee biochar (Biochar) compared to carbon black (CB) as a partial replacement for carbon black in epoxidized natural rubber (ENR). Particle size and elemental analysis were used to characterize the biochar and CB. Cure characteristics, tensile, thermal, and morphological properties on the effect of biochar and CB as filler were studied. It was found that incorporating 10 phr of spent coffee biochar could improve the composites' tensile properties and thermal performance compared to carbon black. However, the addition of biochar significantly affects the maximum torque compared to CB and delays the vulcanization time. SEM study shows that biochar has a strong effect on the morphology of composite films. The FTIR graph reveals no substantial difference between compounds with biochar and CB. According to the thermal calorimetric study, the thermal stability of ENR-Biochar is higher than that of ENR-CB. Additionally, these findings suggest that the utilization of spent coffee as a sustainable biochar could be further explored, but little has been done in epoxidized natural rubber (ENR).

Indexed as

biocharcarbon blackepoxidized natural rubberspent coffee

Identifiers

PMID34451334
PMCPMC8400960
OpenAlexW3193393270

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.