Evidence map›Paper›PMID 34549016›Full record

ArticleBiomass conversion and biorefinery2021

Adsorption of uremic toxins using biochar for dialysate regeneration.

Wei Wang, Zhijuan Wang, Kai Li, Yuxin Liu, Delong Xie, Shaoyun Shan, Liang He, Yi Mei

Abstract read
In one paragraph

Article in Biomass conversion and biorefinery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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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

8 authors.

Wei WangFaculty of Chemical Engineering, Kunming University of Science and Technology, Kunming, China.
Zhijuan WangHigher Educational Key Laboratory for Phosphorus Chemical Engineering of Yunnan Province, Kunming, China.
Kai LiFaculty of Chemical Engineering, Kunming University of Science and Technology, Kunming, China.
Yuxin LiuFaculty of Chemical Engineering, Kunming University of Science and Technology, Kunming, China.
Delong XieFaculty of Chemical Engineering, Kunming University of Science and Technology, Kunming, China.
Shaoyun ShanFaculty of Chemical Engineering, Kunming University of Science and Technology, Kunming, China.
Liang HeFaculty of Chemical Engineering, Kunming University of Science and Technology, Kunming, China.
Yi MeiFaculty of Chemical Engineering, Kunming University of Science and Technology, Kunming, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Numerous studies have shown that patients with COVID-19 have a high incidence of renal dysfunction. However, the dialysis supplies, including dialysates, are also severely inadequate in hospitals at the pandemic centers. Therefore, there is an urgent need to develop materials that can efficiently and rapidly remove toxins and thus regenerate dialysate to make this vital resource remains readily available. In this work, by simple carbonization and activation treatment, the porous activated carbon from waste rubber seed shell (RAC) was prepared. The adsorption results showed that the maximum adsorption capacities of the obtained RAC for creatinine and uric acid were 430 mg/g and 504 mg/g, respectively. Significantly, the adsorption process can be close to the equilibrium state within 0.5 h, which proved the ultra-fast adsorption response capacity of RAC. Further, the thermodynamics analysis results showed that both the creatinine and uric acid adsorption processes were monolayer, exothermic, and spontaneous. The adsorption kinetics results indicated that the adsorption process of the two uremic toxins followed the pseudo-second-order rate model and was dominated by chemisorption. The instrument analysis results reflected the efficient adsorption of the RAC for the above uremic toxins which might be due to the dipole-dipole interaction between the dipolar oxygen-containing groups of the surface of RAC and the dipoles of the toxins. Moreover, the formed hydrogen bonds between the oxygen groups and the toxins also played an important role. In all, the as-prepared RAC has the potential to efficiently remove major toxins from the dialysate and can be used in in vitro dialysis of numerous patients during the current COVID-19 pandemic.

Indexed as

Adsorption mechanismCreatinineRubber seed shell activated carbonUric acid

Identifiers

PMID34549016
PMCPMC8445020

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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.