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Smart Wearable Technology: Thermal Management and Energy Applications
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Smart Wearable Technology: Thermal Management and Energy Applications

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: closed (15 January 2025) | Viewed by 22711

Special Issue Editors

Dr. Zhanxiao Kang

E-Mail Website
Guest Editor
School of Fashion and Textiles, The Hong Kong Polytechnic University, Kowloon, Hong Kong 999077, China
Interests: heat and mass transfer; CFD; thermal management; flexible heat pipe; thermal comfort; smart textiles
Dr. Qing Chen

E-Mail Website
Guest Editor
Shanghai International Fashion Innovation Center, Donghua University, Shanghai 200051, China
Interests: smart fabrics; smart garments; smart shoes; smart protective equipment; smart 3D structures; smart 3D printing; heat and moisture management

Special Issue Information

Dear Colleagues,

Health and energy are the two main problems humankind faces in the 21st century, and smart wearable technology can play an important role in human health and energy applications. Especially when heat/cold waves strike human society more frequently due to global warming, people tend to suffer from an increasing number of temperature-related illnesses and even death. Smart wearable technology can take advantage of body heat or external energy to improve human thermal comfort and health. Additionally, smart wearable technology only heats or cools the microclimate around the human body instead of the whole room space, which can reduce building energy consumption and greenhouse gas emissions.

Furthermore, body heat could also be used to generate electricity either through thermoelectric elements or via sweat evaporation from clothing fabrics. In addition, the energy of body motion also could be harvested and converted into electricity through the electromagnetic effect, piezoelectric effect, or triboelectric effect. With these effects, smart wearable technology could be used to generate electricity for wearable sensors used to monitor human health. Hence, smart wearable technology can contribute to human health and alleviate global warming through human body thermal management as well as energy harvesting, conversion, and storage.

This Special Issue, entitled “Smart Wearable Technology: Thermal Management and Energy Applications”, will focus on the relevant smart wearable technologies, including, but not limited to, human body thermal and moisture management, radiative cooling, phase change materials, thermal comfort, sweat evaporation, evaporation induced electricity generation, thermoelectric effects, piezoelectricity, triboelectricity, etc. Research papers, communications, and reviews are all welcome.

Dr. Zhanxiao Kang
Dr. Qing Chen
Guest Editors

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. Processes is an international peer-reviewed open access monthly 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 2400 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

  • thermal and moisture management
  • phase change materials
  • thermal comfort
  • energy harvesting
  • energy conversion
  • energy storage
  • evaporation and condensation
  • evaporation-induced electricity generation
  • thermoelectric effects
  • piezoelectricity
  • triboelectricity
  • solar energy

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Published Papers (12 papers)

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Research

Jump to: Review

15 pages, 4516 KiB  
Article
Optimization of Deep Learning Models for Enhanced Respiratory Signal Estimation Using Wearable Sensors
by Jiseon Kim and Jooyong Kim
Processes 2025, 13(3), 747; https://doi.org/10.3390/pr13030747 - 4 Mar 2025
Viewed by 209
Abstract
Measuring breathing changes during exercise is crucial for healthcare applications. This study used wearable capacitive sensors to capture abdominal motion and extract breathing patterns. Data preprocessing methods included filtering and normalization, followed by feature extraction for classification. Despite the growing interest in respiratory [...] Read more.
Measuring breathing changes during exercise is crucial for healthcare applications. This study used wearable capacitive sensors to capture abdominal motion and extract breathing patterns. Data preprocessing methods included filtering and normalization, followed by feature extraction for classification. Despite the growing interest in respiratory monitoring, research on a deep learning-based analysis of breathing data remains limited. To address this research gap, we optimized CNN and ResNet through systematic hyperparameter tuning, enhancing classification accuracy and robustness. The optimized ResNet outperformed the CNN in accuracy (0.96 vs. 0.87) and precision for Class 4 (0.8 vs. 0.6), demonstrating its capability to capture complex breathing patterns. These findings highlight the importance of hyperparameter optimization in respiratory monitoring and suggest ResNet as a promising tool for real-time assessment in medical applications. Full article
(This article belongs to the Special Issue Smart Wearable Technology: Thermal Management and Energy Applications)
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Figure 1

Figure 1
<p>A schematic of the proposed work.</p> Full article ">Figure 2
<p>Abdominal movement in response to breathing [<a href="#B23-processes-13-00747" class="html-bibr">23</a>].</p> Full article ">Figure 3
<p>(<b>a</b>) Wearable sensors in the shape of a finished garment [<a href="#B23-processes-13-00747" class="html-bibr">23</a>]; (<b>b</b>) the effect of single-ply and triple-ply thread length on the resistance value.</p> Full article ">Figure 4
<p>(<b>a</b>) Schematic of LCR meter [<a href="#B23-processes-13-00747" class="html-bibr">23</a>]; (<b>b</b>) measurement of wearable sensors.</p> Full article ">Figure 5
<p>(<b>a</b>) CNN architecture; (<b>b</b>) ResNet architecture.</p> Full article ">Figure 6
<p>Breathing data under different conditions: (<b>a</b>) resting; (<b>b</b>) low intensity; (<b>c</b>) moderate intensity; and (<b>d</b>) high intensity.</p> Full article ">Figure 7
<p>(<b>a</b>) Training results window; (<b>b</b>) confusion matrix (CNN-1).</p> Full article ">Figure 8
<p>The results of O-CNN. (<b>a</b>) Validation accuracy; (<b>b</b>) training loss.</p> Full article ">Figure 9
<p>(<b>a</b>) Training results window; (<b>b</b>) confusion matrix (ResNet-1).</p> Full article ">Figure 10
<p>The results of O-Resnet. (<b>a</b>) Validation accuracy; (<b>b</b>) training loss.</p> Full article ">Figure 11
<p>Confusion matrix of O-CNN.</p> Full article ">Figure 12
<p>Confusion matrix of O-ResNet.</p> Full article ">
12 pages, 5706 KiB  
Article
The Development and Optimization of a Textile Image Processing Algorithm (TIPA) for Defect Detection in Conductive Textiles
by Sang-Un Kim and Joo-Yong Kim
Processes 2025, 13(2), 486; https://doi.org/10.3390/pr13020486 - 10 Feb 2025
Viewed by 481
Abstract
This study introduces a Textile Image Processing Algorithm (TIPA) designed to detect defects in conductive textiles, a crucial element of wearable technology. TIPAs employ image preprocessing, filtering, and classification to identify issues like uneven distribution of conductive particles. When applied to fabrics produced [...] Read more.
This study introduces a Textile Image Processing Algorithm (TIPA) designed to detect defects in conductive textiles, a crucial element of wearable technology. TIPAs employ image preprocessing, filtering, and classification to identify issues like uneven distribution of conductive particles. When applied to fabrics produced via dip-coating, our TIPA was optimized using a threshold ratio, achieving over 85% accuracy, with a maximum of 100% under ideal conditions. However, detection challenges were noted in fabrics with large, diffuse stains, particularly at extreme threshold ratios. This TIPA proves to be a valuable tool for improving quality control in smart textiles, with potential for further optimization. Full article
(This article belongs to the Special Issue Smart Wearable Technology: Thermal Management and Energy Applications)
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<p>Conductive textile in wearable sensor.</p> Full article ">Figure 2
<p>Defect detection through humans and through Textile Image Processing Algorithms.</p> Full article ">Figure 3
<p>Dip-coating process of conductive textile fabrication.</p> Full article ">Figure 4
<p>Images of normal conductive textile and textile with defects.</p> Full article ">Figure 5
<p>TIPA process using conductive textile images.</p> Full article ">Figure 6
<p>Images of preprocessing results for defective conductive textile: (<b>a</b>) histogram equalization image; (<b>b</b>) comparison of original and histogram equalization results; (<b>c</b>) grayscale image with inverted pixel values.</p> Full article ">Figure 7
<p>Images of filtering results for defective conductive textile: (<b>a</b>) pixel value filtering; (<b>b</b>) 1st pixel size filtering; (<b>c</b>) 2nd pixel size filtering.</p> Full article ">Figure 8
<p>Images of TIPA results for normal conductive textile: (<b>a</b>) original image; (<b>b</b>) 1st pixel size filtering; (<b>c</b>) 2nd pixel size filtering.</p> Full article ">Figure 9
<p>Blurred images of defective conductive textile: (<b>a</b>) original image; (<b>b</b>) TIPA result.</p> Full article ">
14 pages, 3586 KiB  
Article
Improving Human Activity Recognition Through 1D-ResNet: A Wearable Wristband for 14 Workout Movements
by Sang-Un Kim and Joo-Yong Kim
Processes 2025, 13(1), 207; https://doi.org/10.3390/pr13010207 - 13 Jan 2025
Viewed by 599
Abstract
This study presents a 1D Residual Network(ResNet)-based algorithm for human activity recognition (HAR) focused on classifying 14 different workouts, which represent key exercises commonly performed in fitness training, using wearable inertial measurement unit (IMU) sensors. Unlike traditional 1D Convolutional neural network (CNN) models, [...] Read more.
This study presents a 1D Residual Network(ResNet)-based algorithm for human activity recognition (HAR) focused on classifying 14 different workouts, which represent key exercises commonly performed in fitness training, using wearable inertial measurement unit (IMU) sensors. Unlike traditional 1D Convolutional neural network (CNN) models, the proposed 1D ResNet incorporates residual blocks to prevent gradient vanishing and exploding problems, allowing for deeper networks with improved performance. The IMU sensor, placed on the wrist, provided Z-axis acceleration data, which were used to train the model. A total of 901 data samples were collected from five participants, with 600 used for training and 301 for testing. The model achieved a recognition accuracy of 97.09%, surpassing the 89.03% of a 1D CNN without residual blocks and the 92% of a cascaded 1D CNN from previous research. These results indicate that the 1D ResNet model is highly effective in recognizing a wide range of workouts. The findings suggest that wearable devices can autonomously classify human activities and provide personalized training recommendations, paving the way for AI-driven personal training systems. Full article
(This article belongs to the Special Issue Smart Wearable Technology: Thermal Management and Energy Applications)
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Figure 1
<p>The sensing mechanism for workout recognition using the Z-axis acceleration of an IMU sensor using gravity.</p> Full article ">Figure 2
<p>The wristband with IMU sensor and the red arrows are the Z-axis in the sensor coordinate system.</p> Full article ">Figure 3
<p>The 14 workouts were chosen for recognition. (<b>a</b>) Bench press, (<b>b</b>) Incline bench press, (<b>c</b>) Dumbbell shoulder press, (<b>d</b>) Dumbbell triceps extension, (<b>e</b>) Dumbbell kick, (<b>f</b>) Dumbbell front raise, (<b>g</b>) Lat pull down, (<b>h</b>) Straight arm lat pull down, (<b>i</b>) Deadlift, (<b>j</b>) Dumbbell bent row, (<b>k</b>) One-arm dumbbell row, (<b>l</b>) EZ-bar curls, (<b>m</b>) Machine preacher curl, (<b>n</b>) Seated dumbbell lateral raise [<a href="#B30-processes-13-00207" class="html-bibr">30</a>].</p> Full article ">Figure 4
<p>The main path and skip connection of residual block in ResNet.</p> Full article ">Figure 5
<p>The architecture of 1D ResNet.</p> Full article ">Figure 6
<p>The Z-axis acceleration data of bench press for subject 1.</p> Full article ">Figure 7
<p>The accuracy of training and validation of (<b>a</b>) 1D ResNet and (<b>b</b>) 3D ResNet.</p> Full article ">Figure 8
<p>The confusion matrix of workout recognition 1D ResNet.</p> Full article ">Figure 9
<p>The confusion matrix of workout recognition 1D ResNet fresh test.</p> Full article ">
12 pages, 12717 KiB  
Article
Assessment of Wearable Cooling and Dehumidifying System Used under Personal Protective Clothing through Human Subject Testing
by Yiying Zhou, Lun Lou and Jintu Fan
Processes 2024, 12(6), 1126; https://doi.org/10.3390/pr12061126 - 30 May 2024
Cited by 1 | Viewed by 1413
Abstract
Healthcare professionals wearing personal protective equipment (PPE) during outbreaks often experience heat strain and discomfort, which can negatively impact their work performance and well-being. This study aimed to evaluate the physiological and psychological effects of a newly designed wearable cooling and dehumidifying system [...] Read more.
Healthcare professionals wearing personal protective equipment (PPE) during outbreaks often experience heat strain and discomfort, which can negatively impact their work performance and well-being. This study aimed to evaluate the physiological and psychological effects of a newly designed wearable cooling and dehumidifying system (WCDS) on healthcare workers wearing PPE via a 60 min treadmill walking test. Core temperature, mean skin temperature, heart rate, and subjective assessments of thermal sensation, wetness sensation, and thermal comfort were measured throughout the test. Additionally, ratings of wearing comfort and movement comfort were recorded during a wearing trial. The results showed that the WCDS significantly reduced core temperature, improved thermal sensation, and reduced wetness sensation compared to the non-cooling condition. The microclimatic temperature within the PPE was significantly lower in the cooling condition, indicating the WCDS’s ability to reduce heat buildup. The wearing trial results demonstrated general satisfaction with the wearability and comfort of the WCDS across various postures. These findings contribute to the development of enhanced PPE designs and the improvement in working conditions for healthcare professionals on the frontlines during outbreaks. Full article
(This article belongs to the Special Issue Smart Wearable Technology: Thermal Management and Energy Applications)
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Figure 1
<p>Wearable cooling and dehumidifying system (WCDS).</p> Full article ">Figure 2
<p>A subject wearing the WCDS walking on a treadmill.</p> Full article ">Figure 3
<p>Measuring sites of heart rate, core temperature, skin temperature, and microclimatic temperature.</p> Full article ">Figure 4
<p>Subjective measurement scales: (<b>a</b>) thermal sensation; (<b>b</b>) wetness sensation; (<b>c</b>) thermal comfort.</p> Full article ">Figure 5
<p>Wearing trial scales: (<b>a</b>) wearing comfort; (<b>b</b>) body movement comfort.</p> Full article ">Figure 6
<p>Six different postures for body movement comfort evaluation.</p> Full article ">Figure 7
<p>Comparison of changes in the core temperature (<b>a</b>), mean skin temperature (<b>b</b>), and heart rate (<b>c</b>) between the control and cooling groups; *: <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 7 Cont.
<p>Comparison of changes in the core temperature (<b>a</b>), mean skin temperature (<b>b</b>), and heart rate (<b>c</b>) between the control and cooling groups; *: <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 8
<p>Changes in microclimatic temperature in the upper back area during the whole test period. *: <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 9
<p>Time changes in the whole body-, upper body-thermal sensations (<b>a</b>,<b>b</b>), wetness sensation (<b>c</b>,<b>d</b>), and thermal comfort sensation (<b>e</b>,<b>f</b>) in the control and cooling groups. *: <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 9 Cont.
<p>Time changes in the whole body-, upper body-thermal sensations (<b>a</b>,<b>b</b>), wetness sensation (<b>c</b>,<b>d</b>), and thermal comfort sensation (<b>e</b>,<b>f</b>) in the control and cooling groups. *: <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 10
<p>Wearing (<b>a</b>) and movement (<b>b</b>) comfort votes for subjects wearing WCDS.</p> Full article ">
13 pages, 1524 KiB  
Article
Performance Comparison of High-Temperature Heat Pumps with Different Vapor Refrigerant Injection Techniques
by Yuqiang Yang, Yu Wang, Zhaoyang Xu, Baojiang Xie, Yong Hu, Jiatao Yu, Yehong Chen, Ting Zhang, Zhenneng Lu and Yulie Gong
Processes 2024, 12(3), 566; https://doi.org/10.3390/pr12030566 - 13 Mar 2024
Cited by 2 | Viewed by 1845
Abstract
In order to develop a highly efficient and stable high-temperature heat pump to realize high-efficient electrification in the industrial sector, performance of high-temperature heat pumps with a flash tank vapor injection and sub-cooler vapor injection are compared under different evaporation temperatures, condensation temperatures, [...] Read more.
In order to develop a highly efficient and stable high-temperature heat pump to realize high-efficient electrification in the industrial sector, performance of high-temperature heat pumps with a flash tank vapor injection and sub-cooler vapor injection are compared under different evaporation temperatures, condensation temperatures, compressor suction superheat degrees, subcooling degrees and compressor isentropic efficiencies. The results show that the COP, injection mass flow ratio and VHC of the FTVC are higher than those of the SVIC-0, SVIC-5, SVIC-10 and SVIC-20 under the same working conditions, while the discharge temperature of the FTVC is approximately equal to that of the SVIC-0 and lower than those of the SVIC-5, SVIC-10 and SVIC-20. When the evaporation temperature, the condensation temperature and injection pressure are 55 °C, 125 °C and 921.4 kPa, respectively, the system COP of the FTVC is 4.49, which is approximately 6.7%, 7.3%, 7.8% and 8.9% higher than those of the SVIC-0, SVIC-5, SVIC-10, and SVIC-20, respectively. Full article
(This article belongs to the Special Issue Smart Wearable Technology: Thermal Management and Energy Applications)
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<p>Principle diagram of the vapor injection heat pump with flash tank.</p> Full article ">Figure 2
<p>Principle diagram of the vapor injection heat pump with sub-cooler.</p> Full article ">Figure 3
<p>Performance comparison under different evaporation temperatures.</p> Full article ">Figure 4
<p>Performance comparison under different condensation temperatures.</p> Full article ">Figure 5
<p>Performance comparison under different suction superheat degrees.</p> Full article ">Figure 6
<p>Performance comparison under different subcooling degrees.</p> Full article ">Figure 7
<p>Performance comparison under different isentropic efficiencies.</p> Full article ">
14 pages, 854 KiB  
Article
Evaluation and Prediction of the Effect of Fabric Wetting on Coolness
by Zijiang Wu, Yunlong Shi, Xiaoming Qian and Haiyang Lei
Processes 2023, 11(8), 2298; https://doi.org/10.3390/pr11082298 - 31 Jul 2023
Cited by 1 | Viewed by 1352
Abstract
As an important parameter of garment comfort, the thermal sensation of fabrics changes with factors such as sweat-induced humidity, making it a crucial area of research. To explore the coolness sensation of fabrics under different humidities, we tested heat transfer between fabrics and [...] Read more.
As an important parameter of garment comfort, the thermal sensation of fabrics changes with factors such as sweat-induced humidity, making it a crucial area of research. To explore the coolness sensation of fabrics under different humidities, we tested heat transfer between fabrics and skin for 20 different fabrics with varying thermal absorption rates using fuzzy comprehensive evaluation to objectively assess their coolness levels. Subjective evaluation was then obtained by having subjects touch the fabrics and provide feedback, resulting in a subjective evaluation of their coolness levels. We compared the objective and subjective evaluations and found them to be highly consistent (R2 = 0.909), indicating accurate objective classification of fabric coolness levels. Currently, random forest regression models are widely used in the textile industry for classification, identification, and performance predictions. These models enable the prediction of fabric coolness levels by simultaneously considering the impact of all fabric parameters. We established a random forest regression model for predicting the coolness of wet fabrics, obtaining a high accuracy between predicted and tested thermal absorption coefficients (R2 = 0.872, RMSE = 0.305). Therefore, our random forest regression model can successfully predict the coolness of wet fabrics. Full article
(This article belongs to the Special Issue Smart Wearable Technology: Thermal Management and Energy Applications)
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<p>Random Forest regression model.</p> Full article ">Figure 2
<p>Thermal absorption coefficient class classification results.</p> Full article ">Figure 3
<p>Fitting curves for subjective and objective evaluations.</p> Full article ">Figure 4
<p>Comparison of the consistency between predicted and measured values.</p> Full article ">
16 pages, 1734 KiB  
Article
Modification and Validation of a Dynamic Thermal Resistance Model for Wet-State Fabrics
by Zijiang Wu, Yunlong Shi, Ruiliang Yang, Xiaoming Qian and Shuting Fang
Processes 2023, 11(6), 1630; https://doi.org/10.3390/pr11061630 - 26 May 2023
Cited by 2 | Viewed by 1895
Abstract
To investigate the dynamic thermal resistance of woven fabrics in different wetting states, ten commonly used clothing fabrics were selected and tested for fabric thermal resistance under different levels of water saturation in accordance with Chinese national standards. Based on Mangat’s eight thermal [...] Read more.
To investigate the dynamic thermal resistance of woven fabrics in different wetting states, ten commonly used clothing fabrics were selected and tested for fabric thermal resistance under different levels of water saturation in accordance with Chinese national standards. Based on Mangat’s eight thermal resistance prediction models, the study improved the models by replacing the original moisture content with water content saturation. The suitability of the eight models in predicting the thermal resistance of woven fabrics in wet states was compared using the sum of squared deviations (SSD), sum of absolute deviations (SAD), and correlation coefficient (R2). The results showed that during the process from initial wetting to complete immersion, the measured thermal resistance values of the ten fabric samples were consistent with the predicted values from Model 5 in the theoretical model of thermal resistance (R2 > 0.955). The characteristic of Model 5 is that the air thermal resistance and water thermal resistance are first connected in parallel and then connected in series with the fiber thermal resistance. The corrected predicted values from Model 5 were highly consistent with the experimental measurement values and can be used to approximate the thermal resistance of woven fabrics in wet states. Full article
(This article belongs to the Special Issue Smart Wearable Technology: Thermal Management and Energy Applications)
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Figure 1

Figure 1
<p>Textile heat transfer performance tester (test plate to do highlighting effect).</p> Full article ">Figure 2
<p>Thermal resistance of the fabrics at different water content saturation.</p> Full article ">Figure 3
<p>Thermal insulation rate of the fabrics at different water content saturation.</p> Full article ">Figure 4
<p>Simulated and measured values of the thermal resistance.</p> Full article ">Figure 4 Cont.
<p>Simulated and measured values of the thermal resistance.</p> Full article ">

Review

Jump to: Research

26 pages, 11644 KiB  
Review
Textiles for Very Cold Environments
by Tomasz Blachowicz, Maciej Malczyk, Ilda Kola, Guido Ehrmann, Eva Schwenzfeier-Hellkamp and Andrea Ehrmann
Processes 2024, 12(5), 927; https://doi.org/10.3390/pr12050927 - 1 May 2024
Cited by 2 | Viewed by 1996
Abstract
Textiles are often used to protect people from cold environments. While most garments are designed for temperatures not far below 0 °C, very cold regions on the earth near the poles or on mountains necessitate special clothing. The same is true for homeless [...] Read more.
Textiles are often used to protect people from cold environments. While most garments are designed for temperatures not far below 0 °C, very cold regions on the earth near the poles or on mountains necessitate special clothing. The same is true for homeless people who have few possibilities to warm up or workers in cooling chambers and other cold environments. Passive insulating clothing, however, can only retain body heat. Active heating, on the other hand, necessitates energy, e.g., by batteries, which are usually relatively heavy and have to be recharged regularly. This review gives an overview of energy-self-sufficient textile solutions for cold environments, including energy harvesting by textile-based or textile-integrated solar cells; piezoelectric sensors in shoes and other possibilities; energy storage in supercapacitors or batteries; and heating by electric energy or phase-change materials. Full article
(This article belongs to the Special Issue Smart Wearable Technology: Thermal Management and Energy Applications)
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<p>Schematic representation of phase-change process. Reprinted from [<a href="#B22-processes-12-00927" class="html-bibr">22</a>], Copyright 2008, with permission from Elsevier.</p> Full article ">Figure 2
<p>(<b>a</b>) Thermally stable and unstable layer structures along the symmetry axis; (<b>b</b>) comparison of the computed and measured temperatures at point B at a distance of 0.369 diameters below the center. Blue areas depict the cold, unmolten solid phase, while red areas are hot, molten liquid. Reprinted from [<a href="#B31-processes-12-00927" class="html-bibr">31</a>], Copyright 2009, with permission from Elsevier.</p> Full article ">Figure 3
<p>Fundamental modes of TENGs: (<b>a</b>) Vertical contact-separation mode; (<b>b</b>) lateral sliding mode; (<b>c</b>) single-electrode mode; (<b>d</b>) freestanding triboelectric-layer mode. Reprinted from [<a href="#B49-processes-12-00927" class="html-bibr">49</a>], Copyright 2019, with permission from Elsevier.</p> Full article ">Figure 4
<p>Single thermoelectric pair consisting of <span class="html-italic">n</span>-type and <span class="html-italic">p</span>-type materials. Heat flows from hot side to top side (<span class="html-italic">Q<sub>H</sub></span>→<span class="html-italic">Q<sub>C</sub></span>), and electrical current (<span class="html-italic">I</span>) flows from <span class="html-italic">n</span>-type to <span class="html-italic">p</span>-type material due to a temperature gradient (Δ<span class="html-italic">T</span> = <span class="html-italic">T<sub>Hs</sub></span> − <span class="html-italic">T<sub>Cs</sub></span>). Reprinted from [<a href="#B75-processes-12-00927" class="html-bibr">75</a>], Copyright 2017, with permission from Elsevier.</p> Full article ">Figure 5
<p>(<b>a</b>) Schematic of optimized textile thermopile with optimized leg areas, red and blue lines are connectors to the voltage measuring equipment. (<b>b</b>) Measured open-circuit voltage <span class="html-italic">V<sub>oc</sub></span> (circles) as a function of the temperature gradient Δ<span class="html-italic">T</span> between the hot plate and the cooler, and calculated data for <span class="html-italic">V<sub>oc</sub></span> as a function of Δ<span class="html-italic">T</span>, where Δ<span class="html-italic">T<sub>tc</sub> =</span> Δ<span class="html-italic">T</span> (solid line) and Δ<span class="html-italic">T<sub>tc</sub> =</span> 0.9∙Δ<span class="html-italic">T</span> (dashed line). (<b>c</b>) Measured generated power (circles, red for the 1st thermopile, purple for the 2nd thermopile) as a function of Δ<span class="html-italic">T</span> and calculated generated power (solid lines, grey for the 1st thermopile, blue for the 2nd thermopile) assuming Δ<span class="html-italic">T<sub>tc</sub> =</span> 0.9∙Δ<span class="html-italic">T</span> and electrical contact resistance of 1.2 Ω (1st thermopile) or 1.1 Ω (2nd thermopile) per thermocouple. Reprinted from [<a href="#B78-processes-12-00927" class="html-bibr">78</a>], originally published under a CC-BY license.</p> Full article ">Figure 6
<p>Working principle of a piezoelectric material when applying and releasing pressure. Reprinted from [<a href="#B85-processes-12-00927" class="html-bibr">85</a>], originally published under a CC-BY license.</p> Full article ">Figure 7
<p>Schematic illustrations of energy storage mechanisms of (<b>a</b>) a supercapacitor and (<b>b</b>) a lithium-ion battery. Reprinted from [<a href="#B95-processes-12-00927" class="html-bibr">95</a>], Copyright 2016, with permission from Elsevier.</p> Full article ">Figure 8
<p>Schematic illustration of the fabrication of the N-SiGF sample and the fiber-based supercapacitors (<b>a</b>–<b>c</b>). Reprinted from [<a href="#B99-processes-12-00927" class="html-bibr">99</a>], Copyright 2023, with permission from Elsevier.</p> Full article ">Figure 9
<p>Materials, preparation methods and potential applications of fabric-based supercapacitors. From [<a href="#B113-processes-12-00927" class="html-bibr">113</a>], originally published under a CC-BY license.</p> Full article ">Figure 10
<p>(<b>A</b>) Illustration and design of a flexible zinc-ion battery textile body area network. (<b>B</b>) Fabrication process of the functional fibers as the building blocks. From [<a href="#B124-processes-12-00927" class="html-bibr">124</a>], originally published under a CC-BY-NC license.</p> Full article ">Figure 11
<p>Schematic illustration of the fabrication of NiCo<sub>2</sub>S<sub>4</sub>@rGO nanocomposites, NiCo<sub>2</sub>S<sub>4</sub>@rGO-PU-CNTs fiber cathodes and serpentine footprint fabric Zn-based batteries. PAM: polyacrylamide, CNT: carbon nanotube, PU: polyurethane, rGO: reduced graphene oxide. Reprinted from [<a href="#B128-processes-12-00927" class="html-bibr">128</a>], Copyright 2024, with permission from Elsevier.</p> Full article ">Figure 12
<p>Thermographic images of neat polyacrylates and CS specimens after cooling from 50 °C to RT and maintaining at RT for 3 s, 15 s or 30 s. In each image, the left and the right photo, respectively, correspond to neat polyacrylates and CS specimens ((<b>a</b>) CS-1, (<b>b</b>) CS-2, (<b>c</b>) CS-3 and (<b>d</b>) CS-4). All color scales are from 14 to 40 °C. Reprinted from [<a href="#B135-processes-12-00927" class="html-bibr">135</a>], Copyright 2022, with permission from Elsevier.</p> Full article ">Figure 13
<p>Temperature gradation curves of knitted wool–PA fabric with and without PEG1000-filled PP fibers. Reprinted from [<a href="#B136-processes-12-00927" class="html-bibr">136</a>], originally published under a CC-BY license.</p> Full article ">Figure 14
<p>(<b>a</b>) Optical photograph of active heating textile wrapped around a finger; thermal infrared images at (<b>b</b>) 5 V and (<b>c</b>) 7 V; (<b>d</b>) temperature profiles of active heating textile at 3–12 V; (<b>e<sub>1</sub></b>–<b>e<sub>6</sub></b>) corresponding thermal infrared images at 0–12 V. Reprinted from [<a href="#B148-processes-12-00927" class="html-bibr">148</a>], Copyright 2023, with permission from Elsevier.</p> Full article ">Figure 15
<p>Evolution curves of the 4th toe and the left foot temperatures for females and males in mummy-shaped sleeping bags (conventional: MAR<sub>CON</sub>, heated: MAR<sub>HT</sub>). Significant deviations calculated by two-way ANOVA tests are given with significance levels <span class="html-italic">p</span> &lt; 0.05 (marked * on the graphs) and &lt; 0.01 (**), respectively. From [<a href="#B154-processes-12-00927" class="html-bibr">154</a>], originally published under a CC-BY license.</p> Full article ">
13 pages, 2542 KiB  
Review
Current Research Status and Development Trends of Cooling Suits in High-Temperature Mine Environments: A Review
by Yu Ma, Qing Wan, Zidan Gong, Yiwei Wu and Jie Zhou
Processes 2023, 11(11), 3256; https://doi.org/10.3390/pr11113256 - 20 Nov 2023
Cited by 4 | Viewed by 2123
Abstract
To gain a deeper understanding of the current research status of cooling suits in high-temperature mines, this paper provides separate introductions to vest-type cooling suits and full-body cooling suits. It summarizes the categories of cooling suits based on different cooling media and systematically [...] Read more.
To gain a deeper understanding of the current research status of cooling suits in high-temperature mines, this paper provides separate introductions to vest-type cooling suits and full-body cooling suits. It summarizes the categories of cooling suits based on different cooling media and systematically elucidates the advantages and disadvantages of each type. The paper also analyzes the current application status of cooling suits in mine environments. It suggests that the future research directions for cooling suits in mines include the miniaturization of components, intelligent temperature control, optimization of new phase-change materials, development of cooling fabrics, and research in smart fibers. Full article
(This article belongs to the Special Issue Smart Wearable Technology: Thermal Management and Energy Applications)
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<p>Relationship between clothing microclimate regions and human thermal comfort.</p> Full article ">Figure 2
<p>The relationship between the human body, environment, and cooling clothing.</p> Full article ">Figure 3
<p>Heat exchange diagram between human body and environment.</p> Full article ">Figure 4
<p>Number of publications on temperature-reducing clothing.</p> Full article ">Figure 5
<p>Factors affecting the cooling effect of cooling clothing.</p> Full article ">Figure 6
<p>Structural types of cooling suits. (<b>a</b>) Tank top cooling suit; (<b>b</b>) full-body cooling suit.</p> Full article ">Figure 7
<p>Fan-style cooling suit.</p> Full article ">Figure 8
<p>Style diagram of liquid cooling suit. (<b>a</b>) Front of cooling suit; (<b>b</b>) Back of cooling suit.</p> Full article ">
25 pages, 6864 KiB  
Review
Personal Wearable Thermal and Moisture Management Clothing: A Review on Its Recent Trends and Performance Evaluation Methods
by Junming Zhou, Jinming Zhao, Xiaolei Guo, Yuxing Hu, Xiaofeng Niu and Faming Wang
Processes 2023, 11(11), 3063; https://doi.org/10.3390/pr11113063 - 25 Oct 2023
Cited by 4 | Viewed by 4121
Abstract
Personal wearable systems designed to manage temperature and moisture are gaining popularity due to their potential to enhance human thermal comfort, safety, and energy efficiency, particularly in light of climate change and energy shortages. This article presents the mechanisms of thermal and moisture [...] Read more.
Personal wearable systems designed to manage temperature and moisture are gaining popularity due to their potential to enhance human thermal comfort, safety, and energy efficiency, particularly in light of climate change and energy shortages. This article presents the mechanisms of thermal and moisture management, recent advances in wearable systems for human thermal and moisture management, and methods for their performance evaluation. It evaluates the pros and cons of various systems. The study finds that most wearable systems for thermal and moisture management are being examined as individual topics. However, human heat and moisture management have noteworthy interactions and impacts on human thermal comfort. There are certain limitations in the methods used for evaluating personal heat and moisture management in wearable systems. This review suggests future research directions for wearable systems to advance this field and overcome these limitations. Full article
(This article belongs to the Special Issue Smart Wearable Technology: Thermal Management and Energy Applications)
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<p>Association between work capacity and wet bulb temperature for different work intensities.</p> Full article ">Figure 2
<p>Personal thermal and moisture management [<a href="#B5-processes-11-03063" class="html-bibr">5</a>].</p> Full article ">Figure 3
<p>Thermal transfer mechanism of clothing.</p> Full article ">Figure 4
<p>Moisture transfer mechanism of clothing.</p> Full article ">Figure 5
<p>(<b>a</b>) Duct-type air-cooling clothing [<a href="#B23-processes-11-03063" class="html-bibr">23</a>]; (<b>b</b>) Fan-type hybrid dry ice and air-cooling clothing [<a href="#B25-processes-11-03063" class="html-bibr">25</a>].</p> Full article ">Figure 6
<p>(<b>a</b>) Thermoelectric liquid-cooling clothing [<a href="#B17-processes-11-03063" class="html-bibr">17</a>]; (<b>b</b>) ice liquid-cooling clothing [<a href="#B30-processes-11-03063" class="html-bibr">30</a>].</p> Full article ">Figure 7
<p>Phase-change cooling clothing [<a href="#B3-processes-11-03063" class="html-bibr">3</a>].</p> Full article ">Figure 8
<p>Thermal-radiation cooling clothing [<a href="#B54-processes-11-03063" class="html-bibr">54</a>].</p> Full article ">Figure 9
<p>Hybrid-cooling wearable clothing [<a href="#B66-processes-11-03063" class="html-bibr">66</a>].</p> Full article ">Figure 10
<p>(<b>a</b>) Schematic diagram of evaporative cooling clothing; (<b>b</b>) heat and mass transfer processes involved in evaporative cooling [<a href="#B19-processes-11-03063" class="html-bibr">19</a>].</p> Full article ">Figure 11
<p>Schematic representation of textiles with hierarchical nanofiber networks and Jannus wettability [<a href="#B85-processes-11-03063" class="html-bibr">85</a>].</p> Full article ">Figure 12
<p>Schematic diagram of (<b>a</b>) PCM composite desiccant packages [<a href="#B89-processes-11-03063" class="html-bibr">89</a>] and (<b>b</b>) ACMR for clothing [<a href="#B72-processes-11-03063" class="html-bibr">72</a>].</p> Full article ">Figure 13
<p>Flow diagram of a two−node model of the thermoregulatory system [<a href="#B113-processes-11-03063" class="html-bibr">113</a>].</p> Full article ">Figure 14
<p>Comparison of the model segmentation, and the two-node and multinode models [<a href="#B115-processes-11-03063" class="html-bibr">115</a>].</p> Full article ">
15 pages, 3522 KiB  
Review
Quantitative Comparison of Personal Cooling Garments in Performance and Design: A Review
by Yiying Zhou, Lun Lou and Jintu Fan
Processes 2023, 11(10), 2976; https://doi.org/10.3390/pr11102976 - 14 Oct 2023
Cited by 5 | Viewed by 3302
Abstract
Personal cooling garments (PCGs) have gained increasing attention as a promising solution to alleviate heat stress and enhance thermal comfort in hot and humid conditions. However, limited attention has been paid to the influence of clothing design on cooling performance. This review highlights [...] Read more.
Personal cooling garments (PCGs) have gained increasing attention as a promising solution to alleviate heat stress and enhance thermal comfort in hot and humid conditions. However, limited attention has been paid to the influence of clothing design on cooling performance. This review highlights the influence of design factors and provides a quantitative comparison in cooling performance for different types of PCGs, including air cooling garments, evaporative cooling garments, phase-change cooling garments, and liquid cooling garments. A detailed discussion about the relationship between design factors and the cooling performance of each cooling technique is provided based on the available literature. Furthermore, potential improvements and challenges in PCG design are explored. This review aims to offer a comprehensive insight into the attributes of various PCGs and promote interdisciplinary collaboration for improving PCGs in both cooling efficiency and garment comfort, which is valuable for further research and innovation. Full article
(This article belongs to the Special Issue Smart Wearable Technology: Thermal Management and Energy Applications)
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<p>The schematic diagram of active air cooling garments (ACGs) (e.g., cooling achieved by fan (<b>a</b>) and vortex tube (<b>b</b>)).</p> Full article ">Figure 2
<p>The schematic diagram of evaporative cooling garments (ECGs).</p> Full article ">Figure 3
<p>The schematic diagram of phase-change cooling garments (PCCGs).</p> Full article ">Figure 4
<p>The schematic diagram of liquid cooling garments (LCGs).</p> Full article ">Figure 5
<p>The schematic diagram of thermoelectric cooling garments (TCGs).</p> Full article ">
14 pages, 7994 KiB  
Review
Systematic Evaluation of Research Progress in the Textile Field over the Past 10 Years: Bibliometric Study on Smart Textiles and Clothing
by Ting Wang, Changqing Liu, Jun Zhang and Aosi Wang
Processes 2023, 11(9), 2797; https://doi.org/10.3390/pr11092797 - 20 Sep 2023
Viewed by 2044
Abstract
Intelligent textile clothing is one of the most popular topics in the field. In recent decades, rapid advances have been made in the area of intelligent textile clothing research, and the intellectual structure pertaining to this domain has significantly evolved. We used CiteSpace [...] Read more.
Intelligent textile clothing is one of the most popular topics in the field. In recent decades, rapid advances have been made in the area of intelligent textile clothing research, and the intellectual structure pertaining to this domain has significantly evolved. We used CiteSpace 6.2.R4, VOSviewer 1.6.19, to evaluate and visualize the results, analyzing articles, countries, regions, institutions, authors, journals, citations, and keywords. Both a macroscopic sketch and a microscopic characterization of the entire knowledge domain were realized. The aim of this paper is to utilize bibliometric and knowledge mapping theories to identify relevant research papers on the subject of smart textiles and clothing that have been published by the China Knowledge Network Web of Science (WOS) within the last decade. It is concluded that the main topics of smart textile and garment research can be divided into nine categories: wearable electronics, smart textiles, flexible antennas, energy storage, textile actuators, mechanical properties, asymmetric supercapacitors, carbon nanotubes, and fiber extrusion. In addition to the latter analysis, emerging trends and future research foci were predicted. This review will help scientists discern the dynamic evolution of intelligent textile clothing research as well as highlight areas for future research. Full article
(This article belongs to the Special Issue Smart Wearable Technology: Thermal Management and Energy Applications)
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<p>Flowchart steps of the search strategy.</p> Full article ">Figure 2
<p>Statistical chart of documents issued.</p> Full article ">Figure 3
<p>Map of national and regional cooperation networks.</p> Full article ">Figure 4
<p>WOS Collaborative network of research institutions based on the literature.</p> Full article ">Figure 5
<p>Density view of literature authors.</p> Full article ">Figure 6
<p>WOS keyword co-occurrence map.</p> Full article ">Figure 7
<p>Keyword clustering map.</p> Full article ">Figure 8
<p>WOS keyword time zone map.</p> Full article ">Figure 9
<p>Prominent words in WOS intelligent textile and apparel research, the timeline is shown in blue, and the interval at the time of the burst is shown in red, indicating the start year, the end year, and the duration of the burst.</p> Full article ">
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