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Polymers
Special Issues
Application of Artificial Intelligence in Polymer Composite Materials Research
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Application of Artificial Intelligence in Polymer Composite Materials Research

A special issue of Polymers (ISSN 2073-4360). This special issue belongs to the section "Polymer Physics and Theory".

Deadline for manuscript submissions: 10 June 2024 | Viewed by 2440

Special Issue Editor

Prof. Dr. Jidong Shi

E-Mail Website
Guest Editor
School of Engineering Physics, Shenzhen Technology University, Shenzhen, China
Interests: wearable flexible sensor and piezoelectric catalytic system

Special Issue Information

Dear Colleagues,

Recently, the development of artificial intelligence (AI) has attracted significant attention around the world. Given its superior capability in data processing and analysis, AI has also facilitated the research of materials science by greatly saving the time and efforts required in the design of novel materials and structures, as well as the analysis of material properties using characterization statistics, which brings new opportunities in this subject. In this Special Issue, we focus on the application of AI in the research of composite materials, which includes, but is not limited to, the AI-assisted design of composites for certain properties, prediction of composite properties by AI-related techniques, AI-assisted failure analysis of composites, and AI in the data processing of composite-based electronics. Manuscripts on the recent advances in this field are welcome in this Special Issue.

Prof. Dr. Jidong Shi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Polymers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • composite
  • polymer
  • materials design
  • property prediction
  • data processing
  • failure analysis
  • soft electronics

Published Papers (2 papers)

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Research

10 pages, 2427 KiB  
Article
Welded Carbon Nanotube–Graphene Hybrids with Tunable Strain Sensing Behavior for Wide-Range Bio-Signal Monitoring
by Zixuan Hong, Zetao Zheng, Lingyan Kong, Lingyu Zhao, Shiyu Liu, Weiwei Li and Jidong Shi
Polymers 2024, 16(2), 238; https://doi.org/10.3390/polym16020238 - 15 Jan 2024
Viewed by 782
Abstract
Carbon nanotubes (CNTs) and graphene have commonly been applied as the sensitive layer of strain sensors. However, the buckling deformation of CNTs and the crack generation of graphene usually leads to an unsatisfactory strain sensing performance. In this work, we developed a universal [...] Read more.
Carbon nanotubes (CNTs) and graphene have commonly been applied as the sensitive layer of strain sensors. However, the buckling deformation of CNTs and the crack generation of graphene usually leads to an unsatisfactory strain sensing performance. In this work, we developed a universal strategy to prepare welded CNT–graphene hybrids with tunable compositions and a tunable bonding strength between components by the in situ reduction of CNT–graphene oxide (GO) hybrid by thermal annealing. The stiffness of the hybrid film could be tailored by both initial CNT/GO dosage and annealing temperature, through which its electromechanical behaviors could also be defined. The strain sensor based on the CNT–graphene hybrid could be applied to collect epidermal bio-signals by both capturing the faint skin deformation from wrist pulse and recording the large deformations from joint bending, which has great potential in health monitoring, motion sensing and human–machine interfacing. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Polymer Composite Materials Research)
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14 pages, 9645 KiB  
Article
Corrosion Degree Evaluation of Polymer Anti-Corrosive Oil Well Cement under an Acidic Geological Environment Using an Artificial Neural Network
by Jun Zhao, Rongyao Chen, Shikang Liu, Shanshan Zhou, Mingbiao Xu and Feixu Dai
Polymers 2023, 15(22), 4441; https://doi.org/10.3390/polym15224441 - 17 Nov 2023
Viewed by 800
Abstract
Oil well cement is prone to corrosion and damage in carbon dioxide (CO2) acidic gas wells. In order to improve the anti-corrosion ability of oil well cement, polymer resin was used as the anti-corrosion material. The effect of polymer resin on [...] Read more.
Oil well cement is prone to corrosion and damage in carbon dioxide (CO2) acidic gas wells. In order to improve the anti-corrosion ability of oil well cement, polymer resin was used as the anti-corrosion material. The effect of polymer resin on the mechanical and corrosion properties of oil well cement was studied. The corrosion law of polymer anti-corrosion cement in an acidic gas environment was studied. The long-term corrosion degree of polymer anti-corrosion cement was evaluated using an improved neural network model. The cluster particle algorithm (PSO) was used to improve the accuracy of the neural network model. The results indicate that in acidic gas environments, the compressive strength of polymer anti-corrosion cement was reduced under the effect of CO2, and the corrosion depth was increased. The R2 of the prediction model PSO-BPNN3 is 0.9970, and the test error is 0.0136. When corroded for 365 days at 50 °C and 25 MPa pressure of CO2, the corrosion degree of the polymer anti-corrosion cement was 43.6%. The corrosion depth of uncorroded cement stone is 76.69%, which is relatively reduced by 33.09%. The corrosion resistance of cement can be effectively improved by using polymer resin. Using the PSO-BP neural network to evaluate the long-term corrosion changes of polymer anti-corrosion cement under complex acidic gas conditions guides the evaluation of its corrosion resistance. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Polymer Composite Materials Research)
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