bluuebunny/crossref_metadata_2025 huggingface dataset:
DOES COMBINED ELECTRICAL MUSCLE STIMULATION ON GASTROCNEMIUS MUSCLE TRAINING IMPROVE NEUROMUSCULAR RESPONSE AND SPORTS PERFORMANCE?
Authors: YANG FAN , JIA SHENG-WEI , WEI QIANG , TENG JIN , LAM WING-KAI , YANG SHAOFENG , ZHENG YI
Published: September 2024

The quadricep muscles are commonly targeted for electrical muscle stimulation (EMS), while the gastrocnemius muscles, despite their crucial role in push-off, are often overlooked. The purpose was to investigate whether incorporating EMS into gastrocnemius training could optimize athletic performance. Two groups of athletes received regular gastrocnemius training (Training-only) or combined EMS into training (Training + EMS) over four weeks. The sprint time, jump height, take-off ground reaction forces, ankle and knee joint power, and electromyography were assessed before and after the training sessions. Two-way ANOVA with repeated measures was performed to determine any significant effect. Both training-only and training + EMS groups showed increased jump height and faster sprint time after training ([Formula: see text]). The participants in the two groups also generated higher take-off force and larger ankle and knee powers after training ([Formula: see text]). EMS adds no significant improvement in performance outcomes ([Formula: see text]). This study implicates that both training-only and training + EMS can enhance sports performance and take-off force and lower-limb joint powers of the athletes, but the use of EMS can only induce minimal added improvements on gastrocnemius training.
U-NET MODEL BASED ON CBAM ATTENTION MECHANISM FOR CORONARY ANGIOGRAPHY SEGMENTATION
Authors: ZHANG YUHONG , ZHANG QIAOHUI , GU JUNJIE , YU HONGXIAO , XU DEMIN
Published: September 2024

Coronary artery disease is a prominent contributor to cardiovascular death. Automatic segmentation of Coronary Angiography (CAG) images is crucial for early diagnosis and treatment and holds significant importance in clinical diagnosis, surgical planning, and treatment evaluation. However, due to challenges such as poor image quality, complex backgrounds, and fine vessel structures, the task of automatic segmentation has always been difficult. This study aims to improve the accuracy and robustness of automatic CAG segmentation. Therefore, we propose a UNet model improved with the Convolutional Block Attention Module (CBAM), which enhances the model’s ability to capture important feature areas by combining the Channel Attention Module (CAM) and the Spatial Attention Module (SAM), making the identified vessels completer and more accurate. Specifically, the CBAM module is integrated into each convolutional layer to enhance the feature representation capabilities of the channel and space. We evaluate the CBAM-UNet based on X-ray Angiography Coronary Artery Disease (XCAD) datasets. The experimental results indicate that the improved model’s accuracy increased to 97%, and precision reached 89.95%. The results demonstrate that the improved model’s overall performance on various metrics surpasses traditional methods, with significant improvements in anti-background noise interference and handling complex vascular structures.
EXCAVATION AND EMPIRICAL STUDY OF RISK FACTORS ASSOCIATED WITH STROKE AND DEPRESSION
Authors: SHAO ZEGUO , YUAN CHENDI , WANG FEI , WANG XUEFEI , JIANG YANLIN , WANG YIFEI , WANG XINYUE , GAO XIAOWEN , QIAN QIAOHUI
Published: September 2024

Stroke is an acute cerebrovascular disease characterized by a high incidence, disability rate, recurrence rate, and mortality rate. Depression, as one of the main types of mood disorders, can manifest as symptoms such as a loss of interest in daily activities, reduced energy, diminished self-esteem, insomnia, and decreased appetite. There exists a significant relationship between these two conditions in terms of their pathogenesis. To delve deeper into the factors associated with the occurrence of both diseases, particularly examining the influence of depression on stroke risk, the authors conducted a large-scale investigation and data collection in the population of Shanghai, obtained 5599 valid data points, including information on hypertension, abnormal blood lipid levels, diabetes, atrial fibrillation, body mass index (BMI), a history of stroke, family history of stroke, previous transient ischemic attacks, smoking history, and exercise habits. Ultimately, the authors employed the decision tree C4.5 algorithm to construct a risk analysis model for both stroke and depression, analyzing the risk factors contributing to their occurrence and inferring the correlation between depression and stroke. The results revealed a close association between depression and a history of stroke, BMI, gender, and physical activity. Additionally, in the nonelderly population, psychological factors such as anxiety, depression, mood disorders, and stress were found to be closely linked to stroke onset. In many instances, regular exercise may mitigate the adverse effects of depression on stroke risk.
MBI-TCMR: A TRADITIONAL CHINESE FORMULA RECOMMENDATION DEEP LEARNING MODEL BASED ON MULTI-TASK META LEARNING AND BIOLOGICAL NETWORK STRUCTURE
Authors: MIAO RUI , HAN NA , ZI-HAN DONG , DONG XIN , LU SHANG-HUI , ZHONG BING-JIE , LI LE , OU YANG-DONG , CHEN DONG-WEI , LI JIAN-LIN , DENG REN-LI , YANG KUO , HUANG QING-HUA , LIANG YONG
Published: September 2024

The performance of existing traditional Chinese medicine (TCM) recommendation models is generally poor because of their weak generalization ability, overfitting, and inability to use known biological networks. Therefore, building a TCM recommendation model based on artificial intelligence has currently become an important bioinformatics task. This study aimed to design a multitask meta-learning model with good biological interpretation for TCM formula recommendation (MBI-TCMR) for deep learning regularization. This method was based on the known biological network structure to sparse the deep learning network, solve the overfitting problem of the model, and enhance the biological interpretability of the model. Furthermore, a multi-learning framework based on meta-learning was also proposed. The framework allowed the MBI-TCMR model to mine knowledge of TCM formulas and quickly adapt to different types of TCM formula recommendation tasks. Finally, we used a gradient-based deep learning feature backtracking method to calculate the feature weight for each neuron. This weight could provide valuable explanatory information for researchers to study how the model made its medicine recommendations. We designed three independent experiments. The experimental results showed that the hit ratio (HR), AUC, and recall and precision value of the MBI-TCMR model outperformed the existing TCM formula recommendation models. The MBI-TCMR model’s HR of top 1–10 reached 0.15–0.9 (Gynecologic Disease Dataset). HR was 10 for the MBI-TCMR model, which was an improvement of 11.1% compared with the best baseline model. The bio-enrichment analysis showed that the model exhibited good bio-interpretation. In summary, this study proposed a novel TCM formula recommendation model, which expanded the application of the artificial intelligence model and achieved good results.
EFFECTIVENESS OF PECTORALIS AND LOWER TRAPEZIUS EXERCISE USING PNF IN TREATING ROTATOR CUFF REPAIR WITH ROUNDED SHOULDER POSTURE: A RANDOMIZED CONTROLLED STUDY
Authors: KANG TAE-WOO , KIM BEOM-RYONG
Published: September 2024

This study aimed to determine the effects of pectoralis stretching and lower trapezius strengthening exercises (PSLTSE) using proprioceptive neuromuscular facilitation (PNF) on posture, shoulder range of motion (ROM), pain, and muscle tone in patients undergoing rotator cuff repair (RCR) and rounded shoulder posture (RSP). Following baseline measurements, participants ([Formula: see text]) with RCR were randomized into two groups: an experimental group ([Formula: see text] [11 male and 12 female; age: 56.69 years]), engaged in PNF-PSLTSE, and a control group ([Formula: see text] [12 male and 11 female; age: 54.09 years]), receiving traditional physical therapy. Each group participated in the intervention five times per week for two weeks. The RSP values for postural changes, flexion and abduction ROM, visual analog scale (VAS), and Myoton PRO of both groups were recorded pre- and post-intervention. A paired t-test was performed to compare differences within the groups pre- and post-intervention. An independent t-test was performed to compare differences between the both groups. After the intervention, RSP, ROM, VAS, and muscle tone improved significantly in the experimental and control groups ([Formula: see text]). The experimental group showed more effective changes in the RSP, ROM, VAS, and muscle tone than the control group ([Formula: see text]). The results suggested that PSLTSE using PNF reduced posture-related shoulder pain, and muscle tone and enhanced ROM in RCR patients with RSP.
APPLICATION OF HUMAN STRESS RESPONSE MECHANISM MODEL IN THE EVALUATION OF ECOLOGICAL RESTORATION IN COAL MINES
Authors: LI ZHONGFEI , LI JIANWEI
Published: September 2024

A mathematical model of the stress response mechanism was constructed based on the process and characteristics of human stress response. In this model, the propagation speed of human stress hormones, the time boundary of the stress response process, and the regulatory equation of stress response are provided. An analogy is made between human stress response and ecological restoration, which have intermediary and diffusion properties between influencing factors and outcomes. Based on the human stress response mechanism model, the weights of various indicators affecting the ecological restoration effect of coal mines were calculated, and a comparison of different restoration schemes was obtained. The evaluation process and results confirm the effectiveness of applying the human stress response mechanism model.
EFFECTIVENESS OF ARTHROSCOPIC MICROFRACTURE COMBINED WITH PLATELET-RICH PLASMA COMPLEX IN THE TREATMENT OF KNEE CARTILAGE INJURY
Authors: HE JIN , CHANG JIHUI , LI KAI , QIU XUZHONG , ZENG LINYING , TANG DONGMEI , LUO JIAN , ZHANG SHUANG
Published: September 2024

Background: Combining arthroscopic microfractures (MF) with platelet-rich plasma (PRP) complexes is a potential approach for treating knee cartilage injuries. However, effective treatment of cartilage injuries in the knee joint remains a challenge. Objective: To evaluate the clinical effects of a platelet-rich complex based on MF surgery for treating knee cartilage injuries. Methods: We selected 120 patients with knee cartilage injury and divided them into observation and experimental groups according to the use of a platelet-rich complex. Preoperative and postoperative follow-up visit data were retrospectively collected and analyzed. Results: Preoperative visual acuity scores between the two groups were comparable ([Formula: see text]). At 3 and 6 months postoperatively, the scores were lower in the experimental group than in the control group ([Formula: see text]). The preoperative Lysholm knee scores and Tegner activity were similar ([Formula: see text]) but greater in the experimental group at 3 and 6 months postoperatively ([Formula: see text]). Postoperative complications were similar between the two groups according to Fisher’s exact test ([Formula: see text]). Conclusion: The platelet-rich complex based on MF surgery treatment of knee cartilage injuries exhibited a better clinical effect and is an easy-to-operate and effective method worthy of clinical consideration.
RESEARCH PROGRESS OF VIRTUAL REALITY TECHNOLOGY IN POSTOPERATIVE PAIN MANAGEMENT
Authors: XIANG LIAN , YANG JUN JIE OU , CHEN XINRONG
Published: September 2024

Post-surgical pain is commonly experienced by patients, and traditional pain medication has limitations and side effects. Virtual reality (VR) is a computer-generated technology that simulates realistic environments, which distracts the patient’s attention and emotions, reduces pain, and improves rehabilitation by creating an immersive experience. The increasing affordability and quality of portable VR headsets, as well as the increasing utility of pain management, bode well for the exciting future of using VR for analgesia. VR was found to be effective as a complementary adjunct or alternative nonpharmacological analgesic to a range of acute and chronic pain treatments. The application of VR to post-surgical pain management can significantly reduce patients’ dependence on medications, as well as decrease the intensity and duration of pain. In this paper, a thorough literature review was conducted to systematically evaluate the use of VR in post-surgical pain management.
A NOVEL METHOD FOR ENTREPRENEURIAL STUDENT MENTAL HEALTH ASSESSMENT USING EEG SIGNAL ANALYSIS ENHANCED BY GENERATIVE ADVERSARIAL NETWORKS
Authors: MENG XIANGMIN , LIU JIAPENG , ZHANG JIE
Published: September 2024

Objective: This study aims to evaluate the mental health of college students who have failed in entrepreneurship by analyzing their electroencephalogram (EEG) signals. Long-term negative emotions are closely related to mental health problems. This study explores how to evaluate the mental health of college students who have failed in entrepreneurship through EEG signals and the use of generative adversarial networks (GANs) to improve classification accuracy. Methods: We first collected EEG data from college students who have failed in entrepreneurship. These data were used to extract multiple features, including time domain features, frequency domain features, and nonlinear features. To improve the accuracy and robustness of the classification model, we introduced GAN. GAN enhances the diversity of the training data set by generating more samples, thereby improving the performance of the EEG signal classification model. This process includes training the generator to generate samples similar to real data and training the discriminator to distinguish between real and generated data. Through this adversarial process, the classification model is continuously optimized. Experimental results show that the EEG signal analysis method based on GAN can effectively classify the long-term emotional state of college students who have failed in entrepreneurship and accurately identify negative emotions such as depression and anxiety. The additional samples generated by GAN significantly improve the classification model’s ability to identify emotional states, making the assessment of mental health status more accurate. Conclusion: This study successfully improved the accuracy of EEG signal classification by introducing GAN technology, thus providing an effective tool for evaluating the mental health status of college students who failed in entrepreneurship. This method not only enhances the ability to detect negative emotions of college students but also provides a novel and practical solution for mental health assessment and intervention in colleges and universities, which helps to detect and intervene in potential mental health problems of college students who failed in entrepreneurship as early as possible.
ANALYSIS OF HUMAN BODY POSTURE DURING ICE SPORTS BASED ON DEEP LEARNING
Authors: WANG XU , GAO HUANYU
Published: September 2024

To better analyze human posture in ice sports, a method based on deep learning has been established. In this method, the input gate, forget gate and output gate are configured to make them more suitable for human pose analysis relying on the LSTM network framework. The input data of LSTM are encoded using the sequence of human skeleton and joint angles in ice skating. During the experiment, abstract models of human bones and joints were provided, with continuous jumping movements in figure skating as the analysis object. LSTM provided detailed analysis results. The experimental results show that the accuracy of deep learning continues to improve in analyzing human posture during ice sports with the number of iterations increasing.
PERIODONTITIS RISK FACTOR ANALYSIS AND MACHINE LEARNING PREDICTION MODEL CONSTRUCTION BASED ON MULTIDIMENSIONAL DATA
Authors: JIN DI , LAN Q. , XU ZHIQIANG
Published: September 2024

This study leveraged a large-scale dataset from NHANES 2013–2014 to gain insights into periodontitis pathogenesis and develop predictive tools. After cleaning and preprocessing the data, 15 crucial factors were identified from over 100 potential risk factors and utilized as input features for four machine learning algorithms: support vector machines (SVM), random forest (RF), neural network and XGBoost. The models were evaluated for periodontitis prediction performance through internal validation metrics such as specificity, accuracy, precision, recall and accuracy (area under the curve (AUC)). Notably, education level, household income and smoking status emerged as key risk factors, aligning with medical literature. While SVM and RFs excelled in specificity and accuracy, neural networks surpassed in precision and recall for periodontitis patients. XGBoost offered a balanced performance, making it a versatile choice. The feature importance analysis underscored the profound influence of socioeconomic factors and unhealthy lifestyle habits on periodontal health. This study contributes novel approaches and insights for periodontitis prevention and treatment, demonstrating clinical and societal significance. Future research should focus on optimizing and externally validating the model to enhance its generalizability and accuracy.
SINGLE-CYCLE PULSE WAVEFORM RECOGNITION METHOD BASED ON SVM
Authors: LI YANG , GENG XINGGUANG , YAO FEI , WANG XIAOYU , WANG YUNFENG , ZHANG YITAO , WANG HAO
Published: September 2024

The single-cycle pulse waves’ morphological characteristics can reflect rich physiological and pathological information about the human cardiovascular system, playing an important role in continuous blood pressure monitoring and cardiovascular disease risk warning. Pulse signals belong to weak physiological signals, and the acquisition process is easily affected by factors such as adjacent channel interference and motion artifacts, leading to abnormal cycles and affecting subsequent feature extraction. Therefore, based on support vector machines (SVM), a single-cycle pulse waveform recognition algorithm is proposed. Using a multi-channel pulse wave acquisition device to collect radial artery pulse signals from 150 subjects. Preprocess the signal to obtain a single-cycle pulse waveform. A total of 54 frequency domain and time-frequency domain feature parameters were extracted by using Fourier transform and wavelet transform to extract the frequency domain and time-frequency domain features. Use the principal components analysis (PCA) method to reduce parameter dimensions. Using the SVM for model training to achieve the classification of single-cycle effective pulse signals, pseudo noise and interference signals. The results show that the algorithm’s classification accuracy is 97.42%, indicating that the algorithm can effectively identify pulse signals, pseudo noise and interference signals, laying the foundation for modeling and suppressing interference signals in the next step.
Flowing from intersection product to cup product
Authors: Friedman Greg , Medina-Mardones Anibal M. , Sinha Dev
Published: September 2024

We use a vector field flow defined through a cubulation of a closed manifold to reconcile the partially defined commutative product on geometric cochains with the standard cup product on cubical cochains, which is fully defined and commutative only up to coherent homotopies. The interplay between intersection and cup product dates back to the beginnings of homology theory, but, to our knowledge, this result is the first to give an explicit cochain level comparison between these approaches.
Seamless Connectivity with Fuzzy-Based Unmanned Aerial Vehicle for VANET in Congested Zone
Authors: Suganthi Evangeline C. , Lenin Ashmiya , Kumaravelu Vinoth Babu
Published: September 2024

Providing seamless connectivity during transit in vehicular ad hoc networks (VANETs) is the most promising and challenging task. In VANET, the vehicles are able to communicate with other vehicles and also with the infrastructure units such as road side unit (RSU). The coverage range of RSU is not sufficient to cover the whole road. The vehicle can drive to an area which is not covered by any of the RSUs, and it is termed as congested zone (CZ). This will extremely lead to traffic congestion and maximize blocking probability. In order to provide connectivity between the road segments and to avoid long delay in message transmission, unmanned aerial vehicles (UAVs) are placed in CZs, which act as relay nodes. To enhance the connectivity of the vehicles, a fuzzy-based UAV-assisted handoff scheme (F-UAV-HO) is proposed in this work. By adopting fuzzy, the unwanted participation of UAV is limited in non-congested traffic, thereby saving the battery resource. To validate the proposed work, the simulations are executed in Network Simulator 2.35. The proposed system outperforms other conventional UAV-assisted schemes in congested traffic in terms of average end-to-end delay, PDR and throughput.
Cardiovascular Risk is Independently Associated with Body Mass Index in Women with Polycystic Ovarian Syndrome in Lagos, Nigeria
Authors: Emokpae Mathias Abiodun , Van-Lare Temitope Olukemi , Babatunde Elizabeth Moyinoluwa
Published: September 2024

Among female subjects of reproductive age, polycystic ovarian syndrome (PCOS) is a common endocrine disorder that may be linked to a number of health risks for cardiovascular diseases (CVDs). The study aims to determine some CVD risk variables and how they relate to body mass index (BMI) in female subjects with PCOS. Fifty healthy women without PCOS (controls) and 90 women with PCOS between the ages of 18 and 45 were enrolled in the study. Standard methods were used to evaluate the serum sex hormones, lipid profile, troponin-I, highly sensitive C-reactive protein (hs-CRP), fasting blood glucose, and atherogenic indices. Compared to controls, the mean age of women with PCOS was substantially lower ([Formula: see text]). The mean values of BMI, waist circumference, and hip were not significantly different from one another. While the cardiometabolic variables were higher in women with PCOS than in healthy subjects, no significant difference between obese/overweight and nonobese women with PCOS in terms of mean BMI, fasting blood glucose, insulin, atherogenic index of plasma (AIP), troponin-I, hs-CRP, luteinizing hormone (LH), follicle-stimulating hormone (FSH), and estradiol were observed. In PCOS-affected women, AIP ([Formula: see text], [Formula: see text]), lipid accumulation product (LAP) ([Formula: see text], [Formula: see text]), and hs-CRP ([Formula: see text], [Formula: see text]) all showed positive correlations with BMI. Regardless of BMI status, there was an independent correlation between cardiovascular risk factors (CVRFs) and PCOS. This implies that regardless of a woman’s BMI, PCOS may increase her risk of CVD. Treatment combined with lifestyle modifications may be useful in lowering the risk of CVD in PCOS-afflicted Nigerian women. The finding suggests that PCOS itself, independent of weight, may increase the risk of heart disease. Nigerian women with PCOS are at increased risk of CVD, regardless of their BMI, and early detection, prevention, and treatment of CVRFs in women with PCOS is desirable.
Multiscale Crystallization of Inorganic Materials
Authors: Sun Congting , Chen Kunfeng , Xue Dongfeng
Published: September 2024

How to form functional materials has long been the cornerstone in materials physics and chemistry, which stimulates general materials scientists to eagerly explore a variety of physicochemical methods toward fabricating desirable materials. In materials formation, constituent elements undergo related phase transitions from atoms/ions/molecules to colloids, nanocrystals, microcrystals, powders/films, or bulk single crystals. These phase transition processes dramatically limit a series of materials’ characteristics/features/performances, such as structures, sizes, morphologies, and properties/functions. In this paper, the formation of materials is uncovered from the viewpoint of multiscale crystallization, from both theoretical studies and experimental observations. The entire crystallization process includes both nucleation and crystal growth stages, respectively controlling the involved phase transitions among clusters/materials with multiscale features. Taking some typical materials as examples, we illustrated the dominant role of variable materials status with multiple sizes during crystallization processes in their practical applications toward energy storage and sustainability. The compromise/coupling between thermodynamics and kinetics controls variant factors to span multiscale modes during materials formation. Uncovering materials physics and chemistry from the viewpoint of multiscale crystallization may deepen our understanding in materials formation in the micro/mesoscale size range and facilitate function optimizations.
What Is a ‘Bug’?
Authors: Widder David Gray , Goues Claire Le
Published: September 2024

On subjectivity, epistemic power, and implications for computing research.
A Sustainable Model of Cultural Heritage Management for Museums and Cultural Heritage Institutions
Authors: Stamatoudi Irini , Roussos Konstantinos
Published: September 2024

Cultural Heritage Management (CHM) is the main vehicle for helping museums and Cultural Heritage (CH) institutions to face challenges, be resilient to climate change, energy and cost of living crisis, natural hazards and pandemic risks, and ultimately to secure sustainable competitiveness. It can support them to manage change wisely, redefining themselves in a constantly changing world. This paper contributes to the understanding of how to establish a state-of-the-art management model for museums and CH institutions, which takes into account the latest developments in the field. It aims to a) bring forward the main issues and principles that need to be taken into account during the drafting procedure of a CHM plan, b) explore current state-of-affairs of professional practice in the CH sector across Europe, c) offer clear and concise information on the CH sector needs, challenges and bottlenecks, and d) eventually, provide practical recommendations that need to be taken into account so as to support communication between CH professionals and their various audiences, with a special focus on young people and local communities. This study is part of the research project “Redefining the future of cultural heritage, through a disruptive model of sustainability” (ReInHerit), which has received funding from the European Union’s Horizon 2020 research and innovation program (
https://www.reinherit.eu/
).
Crime, Violence, and Criminal Justice in Rural America: Toward a Rural Neighborhood Effect
Authors: Eason John M. , Jensen Sarah , Haimson Chloe E.
Published: September 2024

We review the current scholarship on rural policing, punishment, crime, and reentry. We shift the focus from the “square of crime” to an expansive understanding of crime and punishment in rural communities that uses neighborhood effects to study inequality across places. A central focus of the article is an investigation of the prison boom or the tripling of prison facilities in the United States. Ultimately, the prison boom is largely a rural phenomenon. As such, we examine how prison building is a product of carceral capacity tied to rurality and race. By focusing on the neighborhood effect, we can theorize what contributes to, and mitigates, crime and punishment across rural communities. In building toward a theory of a rural neighborhood effect, we investigate context through understanding the role of spatial and racial stratification in shaping inequality across rural places.
Extreme Risk Protection Orders in the United States: A Systematic Review of the Research
Authors: Zeoli April M. , Frattaroli Shannon , Aassar Mia , Cooper Chiara E. , Omaki Elise
Published: September 2024

Extreme risk protection orders (ERPOs) are designed to protect against firearm violence and are a relatively new policy tool in the United States. As of April 2024, 21 states and the District of Columbia have enacted ERPO laws. This article presents a systematic review of the literature concerning ERPO laws, their use, and their effectiveness. Four main domains are considered in relation to the literature landscape: (a) formulation and passage of ERPO laws, (b) legal analyses of ERPO laws, (c) ERPO implementation, and (d) evaluations of ERPO laws. Thirty-six articles met our inclusion criteria and were analyzed. This systematic review provides the existing evidence regarding ERPO implementation and effectiveness and carves a pathway forward for future research, policy, and practice.
Evaluating the Removal of Polystyrene Latex Particles Using the Quartz Transducer Ultrasonic Cleaners in the Megahertz Frequency Range
Authors: Seike Yoshiyuki , Takagi Rai , Hikita Tomomi , Honda Yuji , Taoka Noriyuki , Ichino Yusuke , Mori Tatsuo
Published: September 2024

The removal of nanometer-sized particles is necessary along with a shift to microfabrication and highly integrated semiconductor chips. Spray-type ultrasonic cleaning methods are currently used to clean semiconductors. However, this method has the problem of collapsing wiring patterns due to severe fluctuations in the sound pressure impinging on the substrate surface. We developed a quartz transducer ultrasonic cleaner (QTC) to solve the problem of constant variations in sound pressure. In this study, we conducted experiments on the removal polystyrene latex particles from the surface of silicon wafers using QTC and elucidated the physical particle removal mechanism.
Study of Chemical-Physical Cleaning Technologies for Better Particle Removal Trapped on the Crystal Defects of 4H-SiC
Authors: Ha Geu-Rim , Ra Yoon-Ji , Gye Yeonje , Park Jingoo , Kim Tae-Gon , Lee Juyeol , Yang Jooyoung , Kim Tae-Uk
Published: September 2024

Silicon carbide (SiC) stands out as a superior power semiconductor material due to its exceptional physical properties. However, conventional RCA cleaning methods are ineffective for SiC wafers. This study evaluates the efficacy of chemical-mechanical cleaning techniques, specifically PVA brush scrubbing and megasonic cleaning with hydrogen dissolved deionized water (DIW), in removing particle contaminants from SiC wafers. Findings reveal that while both methods effectively clean the wafer surface, megasonic cleaning with hydrogen dissolved DIW excels in removing particles trapped in crystal defects due to enhanced bubble cavitation. Thus, it is recommended for optimal SiC wafer cleaning.
Rate Equation for TiN Etching in Dilute H2O2
Authors: Ueda Dai , Harumoto Akiko
Published: September 2024

A rate equation for etching of TiN using hydrogen peroxide solution was proposed. TiN etching process could be modeled as a first order reaction by H2O2 and HO2
− concentration over pH range (0 - 9), suggesting that dissolution reaction is rate limiting step. Our finding is that HO2
− reacts approximately 100 times faster than that of H2O2.
Evaluation of Band Structure of Single Crystalline Si-Rich SiSn thin Film
Authors: Ishizaki Hioki , Yokogawa Ryo , Ito Yuta , Minowa Takuya , Kurosawa Masashi , Ogura Atsushi
Published: September 2024

We evaluated the bandgap energy and optical properties of single-crystalline silicon-tin (Si1-x
Sn
x
) thin films utilizing Photoluminescence (PL), spectroscopic ellipsometry (SE), and X-ray photoelectron spectroscopy (XPS). The PL and SE results showed that the indirect and direct bandgap energy decrease with increasing Sn fraction. Furthermore, the change in the direct bandgap energy is larger than the indirect bandgap energy. This shows that Si1-x
Sn
x
approaches the direct transition type. Then, the Sn fraction from the indirect-to-direct bandgap is estimated to be 47.2%. In addition, XPS results showed that the difference in valence band maximum between Si1-x
Sn
x
and pure Si increased depending on Sn fraction. This is responsible for the decrease in the indirect and direct bandgap energy.
Boron Delta-Doping in Si : B Stacks By RP-CVD Epitaxy
Authors: Ferrandon Alice , Deprat Fabien , Vives Jeremy , Frauenrath Marvin , Guyader Veronique , Lacomme Caroline , Madaci Ismail , Favre Luc , Berbezier Isabelle
Published: September 2024

Low resistivity materials are required for advanced MOS or equivalent technologies. High boron in-situ doped Si epitaxy is known to create defects that increase the resistivity. Boron delta-doping (Si:B epitaxy alternating with boron saturation layers) is a potential solution to overcome this limitation. Using the delta-doping process at 750 °C in a Reduced Pressure Chemical Vapor Deposition reactor, a resistivity two times lower than that without delta-doping (8.9x10-4 Ω.cm and 1.86x10-3 Ω.cm respectively) was achieved. SIMS characterization measured a total boron concentration of 3.3x1021 at.cm-3. However, the delta-doping processes , as a function of the soaking volume (product of B2H6 flow and saturation time), resulted in low crystal quality and highly stressed layers. Moreover, thin layers were observed by TEM likely due to the formation of the SiBx phase at each saturation layer. For an equivalent soaking volume, a better crystal quality was obtained with a high B2H6 flow and short saturation time.
Polycrystalline inclusion-free growth of thick, lattice-matched SiGeC with high substitutional Carbon concentrations up to 2%
Authors: Frauenrath Marvin , Vives Jeremy , Devaux Matteo , Blondel Claudel , Aybeke Ece , Obeid Baydaa , Duru Romain , Levert Theo , Printemps Tony , Verdier Stephane , Deprat Fabien
Published: September 2024

Lattice-matched SiGeC with ~20% of Ge was grown at 550°C, 10 Torr on a 300 mm Si substrate in an industrial standard Reduced Pressure Chemical Vapor Deposition reactor using commercially available Si2H6/GeH4/SiH3CH3 precursors. Thicknesses exceeding 250 nm resulted in cluster formation in SiGeC with [C]Sub~2.0% and [C]Int~0.4%, with a surface density up to 6.13x106 µm-2. Room temperature photoluminescence measurements showed that the clusters were non-radiative defects and Transmission Electron Microscopy, using the ASTAR system, proved they were polycrystalline with random orientation. Cluster-free growth was achieved for 1 µm thick SiGeC with [C]Sub~1.0% and no interstitial carbon atoms, however, without lattice-matched growth on a Si substrate. Injecting HCl during a co-flow process resulted in a smooth surface with a RMS roughness of 0.23 nm and a reduction of [C]Intfrom ~0.7% down to ~0.1%, close to the detection limit of X-ray Photoelectron Spectroscopy at 0.1%. This led to a significant cluster surface density reduction down to 1.15x104 µm-2. The injection of HCl caused an increase in the Ge concentration from 19.7% up to 28.9% and a non-lattice-matched growth. A Cyclic Deposition Etch (CDE) process resulted in no [C]Int reduction, but significantly reduced the cluster surface density down to 4.60x104 µm-2. CDE-grown SiGeC was lattice-matched to the Si substrate and yielded a smooth surface with a RMS roughness of 0.22 nm. This enabled the growth of high crystalline quality, thick, lattice-matched layers on a 300 mm Si substrate with material properties beyond pure Si.
Piezo Electric Properties of Epitaxial SiGe
Authors: Yamamoto Yuji , Wen Wei-Chen , Inomata Naoki , Corley-Wiciak Agnieszka Anna , Ryzhak Diana , Corley-Wiciak Cedric , Sorge Roland , Tillack Bernd , Ono Takahito , Zhizian Z.
Published: September 2024

Piezo resistivity of B- and P-doped epitaxial Si1-xGex (x=10-30%) is investigated to assess its application potential for thin film strain sensors. The gauge factor (GF) is calculated based on the change in resistivity to the externally induced compressive uniaxial strain along the current flow direction. In the case of B-doped SiGe, the resistivity decreases under the induced compressive strain which may be related to hole mobility enhancement, while no influence on the resistivity of the P-doped SiGe. A significant increase in the GF is observed by lowering B concentration. At the same B concentration, slightly higher GF is observed for higher Ge content. Moreover, the GF is slightly improved by lowering the SiGe growth temperature. The slight improvement may be related to improved crystallinity. These results suggest that the low-doped p-type SiGe deposited at low temperature has reasonable GF and can potentially be applied in strain sensors.
Performance Enhancement and Mechanism Analysis of IGZO Thin-Film Transistors Utilizing Interdigital Structure
Authors: Lu Yupeng , Yan Gangping , Yang Yanyu , Sun Peng , Luo Jie , Bao Yunjiao , Wang Peng , Xu Gaobo , Wang Guilei , Zhao Chao , Yin Huaxiang , Luo Jun
Published: September 2024

This study investigates the issue of leakage current reduction in oxide semiconductor thin-film transistors (TFTs) with interdigital structures. Electrical measurements clearly demonstrate that the leakage current of interdigitated IGZO devices decreases as the on/off ratio increases. To explore the reasons behind the reduction in leakage current, we conducted two-dimensional device simulations. The results confirm that the use of interdigital structures can influence the electric field distribution within the device channel, introducing shielding electric fields. As the number of interlayer structures between the source and drain increases, the shielding electric fields proportionally increase. Additionally, interdigitated IGZO devices exhibit a large on/off ratio exceeding 2.7×1010, an ultra-low leakage current of less than 3×10-16 A/μm, and a high saturation mobility of 9.4 cm2/Vs. These findings provide valuable insights for designing low-power, high-performance devices based on IGZO.
Durability of Highly Active PGM Catalyst MEA Tested Via Nitrogen and Air AST Cycling Under HDV Condition
Authors: Zhu Guangqi , Zhang Qi , Yu Haoran , Cullen David A. , Jeon Sungho , Ortiz Erika , Stach Eric A. , Marina Oana C. , Li Zheng-Hua , Xie Jian
Published: September 2024

PEMFCs are widely considered as the most promising power sources, particularly for heavy-duty vehicles (HDVs). Unfortunately, the degradation of MEAs under HDV condition remains insufficiently studied. In this work, we systematically investigated two MEAs with catalysts of Pt nanoparticles (NPs) supported over high surface area carbon black. These MEAs were tested for durability under HDV condition in nitrogen using a DOE AST protocol for 180,000 cycles, which is equivalent to 30,000 hours or 1 million miles of operation. The commercial Catalyst MEA also underwent 6,000 AST cycles in air under M2FCT condition. We comprehensively investigated the degradation of catalysts. Our results indicate that both MEAs undergo continuous performance degradation in H2/air and H2/O2 during the AST cycling in nitrogen, where analysis employing scanning transmission electron microscopy (STEM) and inductively coupled plasma mass spectrometry (ICP-MS) reveal significant degradation behavior for Pt catalysts. The MEA exhibits more significant degradation, especially within mass transfer region, during the AST process in air. This study describes the long-term degradation behavior and mechanism with AST cycling in nitrogen or air governing highly efficient and durable PGM-catalyst MEA design under HDV conditions.
Electrochemical Characteristics of Polymer Electrolyte Fuel Cells Using Pt-Ta-Co Electrocatalysts
Authors: Miyamoto Ryo , Sanami Kojiro , Suzuki Takashi , Kubo Kakeru , Yasutake Masahiro , Noda Zhiyun , Nishihara Masamichi , Matsuda Junko , Lyth Stephen Matthew , Sasaki Kazunari
Published: September 2024

Polymer electrolyte fuel cells for heavy-duty applications need highly active and durable electrocatalysts. For this purpose, electrocatalysts based on Pt-Ta-Co materials are here developed. This study evaluates the cell performance, the various overvoltages, start-stop and load cycling durability, and electrocatalyst layer microstructure under different fabrication conditions. Guidelines for higher power, higher performance, and higher durability of polymer electrolyte fuel cells are proposed. Using Pt7Ta2Co1/KB electrocatalysts, the initial catalytic activity, start-stop cycle durability, and load cycle durability were all significantly improved compared with a conventional Pt/C electrocatalyst.
Improvement of Mesoporous Carbon Supports for PEFC
Authors: Doi Hiroki , Hutapea Yasir Arafat , Hayashi Akari
Published: September 2024

New mesoporous carbon (MC) bulk was developed and used as a cathode support, and then 45% Pt/new MC cathode was prepared. Current-voltage (IV) performance of a membrane electrode assembly (MEA) with 45% Pt/new MC cathode was much improved at the high current density area in comparison to a MEA with 33% Pt/conventional MC and also to a standard MEA. Reasons for this improvement were considered in this study. The increased ionomer/carbon (I/C) ratio in the cathode layer was found to be one of the factors for improving IV performance. Also, increase in Pt loading percent against the carbon support came to another factor, which lead to a thinner catalyst layer followed by reducing activation overvoltage and diffusion overvoltage. However, the most important factor for the improvement in the high current density area was found to be most likely the size of mesopores.
Microstructural Design of PEFC Electrocatalyst Layer Using Mesoporous Carbon
Authors: Suzuki Takashi , Miyamoto Ryo , Yasutake Masahiro , Noda Zhiyun , Lyth Stephen Matthew , Nishihara Masamichi , Matsuda Junko , Sasaki Kazunari
Published: September 2024

Heavy-duty applications of polymer electrolyte fuel cells (PEFCs) require improved activity and durability of the cathode electrocatalysts. The nanostructure and microstructure of the catalyst layer both significantly affect PEFC performance. Mesoporous carbons (MC) have mesopores which have been shown to suppress ionomer poisoning, when used as a catalyst support, leading to higher catalytic activity. Here, we compare the initial performance of single cells using Ketjen black (KB) and MC catalyst supports. Furthermore, the fuel cell performance is evaluated using Pt-Ta-Co alloy-based catalyst decorated on MC. Pt-Ta-Co/MC electrocatalyst exhibits high cell performance in a low current density range. However, cell performance at high current densities has yet to be improved due to non-uniform microstructure of the Pt-Ta-Co/MC electrocatalyst layer.
Self-Supporting Microporous Layer for Polymer Electrolyte Fuel Cells Using Thin Carbon Mesh Gas Diffusion Layer
Authors: Yoshikawa Makoto , Noda Zhiyun , Yasutake Masahiro , Kitahara Tatsumi , Lyth Stephen Matthew , Matsuda Junko , Nishihara Masamichi , Sasaki Kazunari
Published: September 2024

The gas diffusion layers (GDLs) used in polymer electrolyte fuel cells (PEFCs) are relatively thick compared with the electrocatalyst layer and electrolyte membrane. Reducing GDL thickness is expected to reduce gas transport resistance. Such thin GDLs with high electrical conductivity enable higher power density per volume of cell stacks. Here, thin MPL/GDLs are fabricated by printing a highly conductive microporous layer (MPL) on carbon meshes or carbon nanotube sheets as a substrate to create tailored self-supporting MPLs. The electrical conductivity was improved by optimizing the carbon black material used in the MPL, and carbon nanotubes were added to tailor a pore structure within the MPL. The thickness of the resulting MPL/GDLs is approaching 50 µm, which is not only thinner than the current MPL/GDL, but also results in lower gas transport resistance. The thinner MPL/GDLs are a promising solution to increase the power density of PEFC cell stacks.
Influence of the Clamping Force and the Formation of Liquid Water Along the Channel on the Local Diffusion Processes in PEMFCs
Authors: Fischer Yannick , Oppek Philipp , Chuang Po-Ya Abel , Weber Andre
Published: September 2024

A novel measurement setup, technique, and segmented model are introduced to spatially resolve mass transport losses along the channel in a PEMFC. The newly designed segmented cell enables not only the measurement of local current and voltage data but also the capture of local temperature and compression levels. By extending the commonly used limiting current method, the local mass transport resistance is determined as a function of local operating parameters. The local current and temperature are directly measured, while the operating conditions along the channel such as pressure, oxygen fraction, and relative humidity are obtained empirically from a 1-D differential model. Leveraging both measured and empirically-based simulation results, this comprehensive approach facilitates the characterization of mass transport resistance distribution along the gas channel. The learnings from this study provide valuable insight into the optimization of channel and materials design for enhancing large active area fuel cell performance and durability.
Electrochemical Impedance Spectra Measurements and Analysis of Polymer Electrolyte Membrane Electrolysis on Short Stack
Authors: Liebert Michael , Joerissen Ludwig , Scholta Joachim
Published: September 2024

Transferring Electrochemical Impedance spectroscopy from fundamental research towards close to application systems allows direct insights into kinetics, transport processes and a state of health indication. Since such stacked systems are optimized for application comprehensive monitoring with reference electrodes is hardly possible. In addition, due to the high power consumption suitable measurement devices are hard to find and the results can only be used to a limited extent. In this work such a test object was set up and tested in different measurement configurations to investigate the reasons for the high noise ratio of the impedance measurement data at more than 200A. It was found that a series circuit consisting of a DC power supply, a high-precision impedance load and the test electrolyser has the least influence on the measurement result of all the methods tested. Finally, the impedance spectra were checked for interpretability using DRT and equivalent circuit fit.
Numerical Modeling of a Polymer Electrolyte Membrane Fuel Cell for Electric Aircraft Propulsion
Authors: Oka Haruya , Katsuki Yusaku , Nakajima Hironori , Inoue Chihiro
Published: September 2024

Electrified propulsion systems with hydrogen-fueled fuel cells can potentially reduce carbon dioxide emissions from aircraft. However, achieving a substantial increase in the gravimetric/volumetric power density of fuel cell systems poses a considerable challenge. In this study, we focus on high temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) working between 100 and 200°C. To design an optimized PEMFC structure for an electric aircraft, we have developed a numerical model that employs the finite element method (FEM) (COMSOL Multiphysics) to couple electrochemistry, gas diffusive and convective transports, and heat transfer in fuel cell components such as separators, gas flow channels, gas diffusion layers, catalyst layers, and a PEM. We thereby derive the current distributions associated with three-dimensional hydrogen, oxygen, and water vapor concentration distributions, as well as temperature distributions. Moreover, we develop a machine learning model from the data outputs of the FEM model, which serves as training data, to reduce the computational costs for the fuel cell stack design incorporated in a system-level numerical model.