Evidence map›Paper›PMID 33266060›Full record

ArticleMicromachines2020

Development of Proportional-Integrative-Derivative (PID) Optimized for the MicroElectric Discharge Machine Fabrication of Nano-Bismuth Colloid.

Kuo-Hsiung Tseng, Chaur-Yang Chang, Yagus Cahyadi, Meng-Yun Chung, Chin-Liang Hsieh

Abstract read
In one paragraph

Article in Micromachines, 2020. 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

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

3 citing papers in PubMed.

  1. Article
  2. Article
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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.

Kuo-Hsiung TsengDepartment of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.ORCID 0000-0001-9284-8401
Chaur-Yang ChangDepartment of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Yagus CahyadiDepartment of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.ORCID 0000-0002-5902-9487
Meng-Yun ChungDepartment of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Chin-Liang HsiehManagement Department, Infosystem Technology, Hsinchu 30062, Taiwan.

Funding

Ministry of Science and Technology, Taiwan MOST 109-2221-E-027-063-
6 · The paper itself

Abstract

Metal nanoparticles are typically prepared by using a chemical method, and a suspension is added to control the particle size and concentration of the nanoparticles. In this study, a micro-electric discharge machine (micro-EDM) was used to melt bismuth into nanoparticles, thus yielding a colloidal solution. No chemicals were added during the manufacturing process, and pure water was used as the medium. The colloid was assessed using an electrohydraulic system, and process parameters were adjusted for optimization; additionally, the discharge pulse wave was analyzed. The proposed preparation process is simple, fast, and cost-effective; moreover, the manufacturing process allows for mass production and reduces environmental pollution. Experimental results revealed that the nano-bismuth (nano-bi) colloidal solution was successfully prepared by the micro-EDM, and absorption peaks in the UV-vis spectrum were observed at 234 and 237 nm. Moreover, to optimize the proportional-integral-derivative (PID) control parameters to be used in the micro-EDM to prepare the nano-bi colloidal solution, this study derived a mathematical model of the micro-EDM. MATLAB was used to obtain the PID parameters. The discharge success rate (74.1876%) for the nano-bi colloidal solution prepared using our method was higher than that (46.9196%) obtained for a nano-bi colloidal solution prepared using an online adaptation method.

Indexed as

electrical spark discharge methodmicro-EDMnano-bismuth colloidPID

Identifiers

PMID33266060
PMCPMC7760149

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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.