Evidence map›Paper›PMID 34577693›Full record

ArticleMicromachines2021

Quantification of Silicon in Rice Based on an Electrochemical Sensor via an Amplified Electrocatalytic Strategy.

Li Fu, Yuhong Zheng, Pengchong Zhang, Guosong Lai

Abstract read
In one paragraph

Article in Micromachines, 2021. 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
–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

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.

Li FuKey Laboratory of Novel Materials for Sensor of Zhejiang Province, College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.ORCID 0000-0002-5957-7790
Yuhong ZhengInstitute of Botany, Jiangsu Province & Chinese Academy of Sciences (Nanjing Botanical Garden Mem. Sun Yat-Sen), Nanjing 210014, China.ORCID 0000-0002-1968-7434
Pengchong ZhangHangzhou Botanical Garden (Hangzhou West Lake Research Institute of Garden Science), Hangzhou 310013, China.
Guosong LaiHubei Key Laboratory of Pollutant Analysis & Reuse Technology, Department of Chemistry, Hubei Normal University, Huangshi 435002, China.

Funding

National Natural Science Foundation of China 22004026
6 · The paper itself

Abstract

Silicon plays a very important role in the growth of rice. The study of the relationship between rice and silicon has become a hot area in the last decade. Currently, the silica-molybdenum blue spectrophotometric method is mostly used for the determination of silicon content in rice. However, the results of this method vary greatly due to the different choices of reducing agents, measurement wavelengths and color development times. In this work, we present for the first time an electrochemical sensor for the detection of silicon content in rice. This electrochemical analysis technique not only provides an alternative detection strategy, but also, due to the rapid detection by electrochemical methods and the miniaturization of the instrument, it is suitable for field testing. Methodological construction using electrochemical techniques is a key objective. The silicon in rice was extracted by HF and becomes silica after pH adjustment. The silica was then immobilized onto the glassy carbon surface. These silica nanoparticles provided additional specific surface area for adsorption of sodium borohydride and Ag ions, which in turn formed Ag nanoparticles to fabricate an electrochemical sensor. The proposed electrochemical sensor can be used for indirect measurements of 10-400 mg/L of SiO

Indexed as

electrocatalytic reductionelectrochemical sensorHF extractionrice tissuesilicon content

Identifiers

PMID34577693
PMCPMC8469415

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Registered trials

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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.