Evidence map›Paper›PMID 42144401›Full record

ArticleScientific reports2026

Bioremediation of low-density polyethylene microplastics using red microalga porphyridium purpureum: a biotechnological perspective.

Hesam Sharif, Roya Mafigholami, Omid Tavakoli, Hamid Moghimi

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Hesam SharifDepartment of Environmental Engineering, WT.C, Islamic Azad University, Tehran, Iran.
Roya MafigholamiDepartment of Environmental Engineering, WT.C, Islamic Azad University, Tehran, Iran. r.mafigholami@wtiau.ac.ir.
Omid TavakoliSchool of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Hamid MoghimiDepartment of Microbiology, School of Biology, University of Tehran, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the present study, the biodegradation of Low-Density Polyethylene microplastics was investigated using Porphyridium purpureum microalgae, with a focus on evaluating the effects of microplastic concentration, particle size, and shaking speed. The study was conducted in two phases: first, optimizing the cultivation conditions for Porphyridium purpureum, followed by assessing its ability to remove Low-Density Polyethylene. The highest microalgae yield was achieved under optimal conditions, which included a light intensity of 2500 µmol photons m⁻² s⁻¹, F/2 culture medium, and a temperature of 30 °C. These conditions were established as the baseline for the Low-Density Polyethylene removal phase. The results demonstrated that the maximum Low-Density Polyethylene removal efficiency achieved was 34.24%. Fourier Transform Infrared Spectroscopy analysis demonstrated a reduction in the intensity of the carbonyl functional group, likely due to the release of carbon from the microplastic structure, thereby indicating an effective interaction between microalgae and Low-Density Polyethylene. Furthermore, Scanning Electron Microscopy images revealed significant surface modifications, including small holes and corrosion on the Low-Density Polyethylene particles, providing strong evidence of the degradation process facilitated by Porphyridium purpureum. These findings suggest that the microalgae have the potential to degrade Low-Density Polyethylene microplastics, offering valuable insights into their use for bioremediation of microplastic pollution.

Indexed as

BiotechnologyMicroalgaeMicroplasticsPolyethylenePorphyridiumBiodegradation, EnvironmentalParticle SizeSpectroscopy, Fourier Transform InfraredTemperatureMicroplasticsPolyethyleneBio-removalLow-Density PolyethyleneMicroalgaeMicroplasticPorphyridium purpureum

Identifiers

PMID42144401
PMCPMC13376653

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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.