Evidence map›Paper›PMID 42146258›Full record

ArticleACS omega2026

Turning EDM Waste into Superhydrophobic Hierarchical Particles with Photothermal Icephobic Properties.

Ping Zhang, Yingjie Xiao

Abstract read
In one paragraph

Article in ACS omega, 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
–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

2 authors.

Ping ZhangGuangdong Engineering Technology Research Center of Ocean Equipment and Manufacturing, School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang, 524088, China.
Yingjie XiaoGuangdong Engineering Technology Research Center of Ocean Equipment and Manufacturing, School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang, 524088, China.ORCID https://orcid.org/0009-0001-9768-9179

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Superhydrophobic surfaces demonstrate efficacy in anti-icing and deicing applications; however, traditional methods for fabricating hydrophobic nanoparticles typically involve intricate synthesis processes, hazardous chemical modifications, and significant energy consumption. This study demonstrates a waste-to-resource strategy, wherein carbonaceous residues generated during electrical discharge machining (EDM) in polyalphaolefin (PAO) are transformed into intrinsically hydrophobic hierarchical particles. SEM, XPS and FTIR analyses reveal that the residues comprise alkane-type organic compounds and metal-derived fragments resulting from PAO decomposition at elevated temperatures, demonstrating intrinsic superhydrophobicity with contact angles of approximately 154°. Superhydrophobic surfaces were constructed by embedding particles into a PDMS adhesive by using a straightforward blade-coating process, eliminating the need for additional nanoparticle synthesis or chemical treatment. The resulting coatings exhibit moderate mechanical durability and robust UV-light stability. Moreover, they possess significant photothermal activity, with temperatures surpassing 45 °C within 15 min under simulated solar illumination. Additionally, they demonstrate superior icephobic properties, increasing the freezing time from approximately 240 s on bare copper to around 950 s and facilitating complete deicing around 27 s. This method provides an efficient, environmentally friendly, and scalable means of producing multifunctional photothermal icephobic coatings while also effectively utilizing EDM waste effectively.

Identifiers

PMID42146258
PMCPMC13176961

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.