Dense Layer Research Articles

Two-phase flow characteristics on porous layer in PEM electrolyzer under different flow channel layouts

As a promising green energy technology, low-temperature proton exchange membrane (PEM) electrolysis exhibits different reaction efficiencies and flow performances under various flow channel structures, but the specific impacts of these different layouts on performance are not well understood. To reveal the influence of the channel layouts, a two-phase PEM electrolysis model is established based on the experiment in this paper. Combing with flow characteristics, current density, and product distribution in the porous transport layers (PTLs) and catalyst layers (CLs), the results indicate that the flows from different directions have a significant impact on the electrolyzer. The vertical flow, perpendicular to the CLs, can greatly alleviate the product accumulation; meanwhile the transverse flow, parallel to the CLs, has a weaker but noticeable effect in gas ventilation. Partial gas will continue to accumulate in the region of weak vertical flow, especially at the end of the transverse flow. Except for the pin-type layout, other structures have a neat and orderly transverse flow within the porous medium, in the direction perpendicular to the flow channel's orientation. This flow pattern leads to product accumulation increasing along the exit direction, as well as product stagnation in the region of weak flow intensity. To facilitate the product discharge, a new flow channel layout, the pyramidal flow layout is proposed, which navigates the flow trend and enhances the convection near the exit. This new layout greatly evens out the current density and product concentration distribution.

Recent progress in improvement in ion cyclotron range of frequencies coupling and power absorption with new antennas of Experimental Advanced Superconducting Tokamak (EAST)

Abstract Efficient ion cyclotron range of frequencies (ICRF) wave heating requires good wave coupling at the plasma edge and good radio frequency power absorption in the plasma core. This study reviews recent progress in improving these two aspects of ICRF heating with the new two-strap antennas through various experiments and simulations on the Experimental Advanced Superconducting Tokamak (EAST). Our study shows that the ICRF coupling can be significantly improved by decreasing the parallel wave number, increasing the local scrape-off layer (SOL) density by midplane gas puffing, and increasing the global SOL density by decreasing the separatrix–antenna distance. It can also be improved by increasing the core plasma density, changing the divertor strike point position, and optimizing the antenna phasing. The core ICRF power absorption can be increased by optimizing the cyclotron resonance position and minority ion concentration and by applying new heating schemes such as three-ion heating. Although some of the methods have been previously studied on other devices, improving ICRF coupling by shifting the divertor strike point was tested on EAST for the first time. Quantitative characterization of these methods and the conclusions drawn from this study can provide important insights for achieving more efficient ICRF heating in current and future fusion machines.

Real-time confinement regime detection in fusion plasmas with convolutional neural networks and high-bandwidth edge fluctuation measurements

A real-time detection of the plasma confinement regime can enable new advanced plasma control capabilities for both the access to and sustainment of enhanced confinement regimes in fusion devices. For example, a real-time indication of the confinement regime can facilitate transition to the high-performing wide-pedestal (WP) quiescent H-mode, or avoid unwanted transitions to lower confinement regimes that may induce plasma termination. To demonstrate real-time confinement regime detection, we use the 2D beam emission spectroscopy (BES) diagnostic system to capture localized density fluctuations of long wavelength turbulent modes in the edge region at a 1 MHz sampling rate. BES data from 330 discharges in either L-mode, H-mode, quiescent H (QH)-mode, or WP QH-mode were collected from the DIII-D tokamak and curated to develop a high-quality database to train a deep-learning classification model for real-time confinement detection. We utilize the 6×8 spatial configuration with a time window of 1024 µs and recast the input to obtain spectral-like features via fast Fourier transform preprocessing. We employ a shallow 3D convolutional neural network for the multivariate time-series classification task and utilize a softmax in the final dense layer to retrieve a probability distribution over the different confinement regimes. Our model classifies the global confinement state on 44 unseen test discharges with an average F 1 score of 0.94, using only ∼1 ms snippets of BES data at a time. This activity demonstrates the feasibility for real-time data analysis of fluctuation diagnostics in future devices such as ITER, where the need for reliable and advanced plasma control is urgent.

Effects of precise cardio sounds on the success rate of phonocardiography.

This work investigates whether inclusion of the low-frequency components of heart sounds can increase the accuracy, sensitivity and specificity of diagnosis of cardiovascular disorders. We standardized the measurement method to minimize changes in signal characteristics. We used the Continuous Wavelet Transform to analyze changing frequency characteristics over time and to allocate frequencies appropriately between the low-frequency and audible frequency bands. We used a Convolutional Neural Network (CNN) and deep-learning (DL) for image classification, and a CNN equipped with long short-term memory to enable sequential feature extraction. The accuracy of the learning model was validated using the PhysioNet 2016 CinC dataset, then we used our collected dataset to show that incorporating low-frequency components in the dataset increased the DL model's accuracy by 2% and sensitivity by 4%. Furthermore, the LSTM layer was 0.8% more accurate than the dense layer.

Optimized SnO2 preparation and coherent interlayers for enhanced charge dynamics and efficient perovskite photovoltaics

The dense, uniform and conformal electron transport layers (ETLs) will largely promote charge separation and extraction. Here, the mixed acid (hydrochloric acid and nitric acid) was used to regulate preparation process and enhance utilization of materials, and the colloids of tin oxide (SnO2) nanocrystals were prepared through hydrothermal process. The complete dissolution of Sn source can increase purity, produce homogeneous precursor, reduce grain sizes and improve film-coverage. As confirmed, a coherent interlayer at the SnO2 ETLs/perovskite interfaces will be achieved by coupling a Cl-bonded SnO2 film with a Cl-containing perovskite precursor. This thin coherent interlayer will largely reduce interface traps, enhance rapid carrier extraction, and impede charge recombination. The uniform polymer phase of (PEO)120-(PPO)30 will be used to passivate traps at the grain boundaries of perovskite films and further improve the photovoltaic performance. The maximum energy conversion efficiency (23.17%, a VOC of 1.153 V, a JSC of 24.75 mA cm−2 and a FF of 0.812) of perovskite solar cells was achieved. The charge separation, extraction, and recombination kinetics (charge dynamic process) was determined by the related characterization techniques. The functionalized SnO2-ETLs and formed coherent interlayer will provide a simple strategy to effectively decrease interface traps, enhance charge extraction, and facilitate development of perovskite solar cells.

Distinct CO2-run-out regime from steric effect of electric double layer in electrochemical CO2 reduction

The field of electrochemical CO2 reduction reaction (eCO2RR) is pursuing high operating current densities, eventually controlled by CO2 transport. Here, we develop a new multiscale modeling approach that is able to more generally describe the effects of the electric double layer (EDL) on CO2 transport over a wide potential window extending to utmost potentials. By leveraging it, we identify a distinct CO2-run-out regime where the supply of CO2 runs out due to the EDL steric effect from a dense layer of solvated cations with the maximum layer thickness equal to the solvated cation size. Consequently, CO2RR current density drops at a relatively negative transition potential generating a bell-shaped polarization curve, which is in contrast to the CO2-transport-limited regime where the current density reaches a plateau. Furthermore, we develop a graphical method, verified by experimental data, to generally predict the transition to the CO2-run-out regime. This work sheds new light on the EDL effects for catalyst design and electrolyzer engineering.

Construction of Fe Nanoparticles Interfacial Layer on Micron Al Surface: Boosting the Efficient Energy Release of High-Energy DAP-4 as a Gradient Catalyst.

The high-energy (H2dabco)[NH4(ClO4)3] (DAP-4) with excellent energetic performance attracts wide attention from researchers. The investigation of its interaction with the Aluminum (Al) is of great importance. However, the higher ignition threshold of DAP-4 and the dense oxide layer (Al2O3) of Al severely limit the energy release efficiency of Al/DAP-4. In this study, a new idea to is first proposed to improve and adjust the thermal decomposition and combustion performance of Al/DAP-4 by constructing a highly dispersed iron (Fe) nanoparticle interfacial layer. It acts as a gradient catalyst to promote the thermal decomposition and combustion of DAP-4 and Al, and it also act as an oxygen transport channel to promote the contact and reaction of oxidizing gases with the internal reactive Al powder. It reduces the thermal decomposition temperature of Al@Fe-3/DAP-4 from 386.30 °C (Al/DAP-4) to 349.48 °C and leads to the vigorous combustion. Theoretical calculations show that Fe nanoparticle interfacial layer can facilitate the transport of oxygen through the established oxygen transport channels, and it can also significantly improve the energetic properties of Al@Fe-3/DAP-4 composites. In conclusion, the new approach is proposed to improve the performance of metal fuel/oxidizer composites by constructing interfacial layers, which is expected to promote their practical applications.

Cryogenic electron microscopy reveals morphologically distinct subtypes of extracellular vesicles among porcine ejaculate fractions

Seminal plasma (SP) is rich in extracellular vesicles (EVs), which are still poorly studied, especially in livestock species. To better understand their functional role in both spermatozoa and endometrial epithelial cells, proper characterization of EVs is an essential step. The objective was to phenotypically characterize porcine seminal EVs (sEVs) using cryogenic electron microscopy (cryo-EM), which allows visualization of EVs in their native state. Porcine ejaculates are released in fractions, each containing SP from different source. This allows characterization sEVs released from various male reproductive tissues. Two experiments were performed, the first with SP from the entire ejaculate (n:6) and the second with SP from three ejaculate fractions (n:15): the first 10 mL of the sperm-rich ejaculate fraction (SRF-P1) with SP mainly from the epididymis, the remainder of the SRF (SRF-P2) with SP mainly from the prostate, and the post-SRF with SP mainly from the seminal vesicles. The sEVs were isolated by size exclusion chromatography and 1840 cryo-EM sEV images were acquired using a Jeol-JEM-2200FS/CR-EM. The size, electron density, complexity, and peripheral corona layer were measured in each sEV using the ImageJ software. The first experiment showed that sEVs were structurally and morphologically heterogeneous, although most (83.1%) were small (less than 200 nm), rounded, and poorly electrodense, and some have a peripheral coronal layer. There were also larger sEVs (16.9%) that were irregularly shaped, more electrodense, and few with a peripheral coronal layer. The second experiment showed that small sEVs were more common in SRF-P1 and SRF-P2, indicating that they originated mainly from the epididymis and prostate. Large sEVs were more abundant in post-SRF, indicating that they originated mainly from seminal vesicles. Porcine sEVs are structurally and morphologically heterogeneous. This would be explained by the diversity of reproductive organs of origin.

Prediction of Daily Climate Using Long Short-Term Memory (LSTM) Model

Climaate prediction plays a vital role in various sectors, including agriculture, disaster management, and urban planning. Traditional methods for climate forecasting often rely on complex physical models, which require substantial computational resources and may not accurately capture local weather patterns. This study explores the potential of Long Short-Term Memory (LSTM) networks, a type of recurrent neural network, for predicting daily climate variables such as temperature, precipitation, and humidity. Utilizing historical climate data from the city of Delhi, we developed an LSTM model to forecast short-term climate trends. The model consists of two LSTM layers followed by three Dense layers and is compiled with the Adam optimizer, mean squared error loss, and mean absolute error as a metric. Our results demonstrate the model's capability to capture temporal dependencies in climate data, achieving a satisfactory level of accuracy in temperature forecasting. This research underscores the potential of machine learning techniques, particularly LSTM networks, in enhancing climate prediction and contributing to more informed decision-making in weather-sensitive sectors.

Enhancing agriculture through real-time grape leaf disease classification via an edge device with a lightweight CNN architecture and Grad-CAM

Crop diseases can significantly affect various aspects of crop cultivation, including crop yield, quality, production costs, and crop loss. The utilization of modern technologies such as image analysis via machine learning techniques enables early and precise detection of crop diseases, hence empowering farmers to effectively manage and avoid the occurrence of crop diseases. The proposed methodology involves the use of modified MobileNetV3Large model deployed on edge device for real-time monitoring of grape leaf disease while reducing computational memory demands and ensuring satisfactory classification performance. To enhance applicability of MobileNetV3Large, custom layers consisting of two dense layers were added, each followed by a dropout layer, helped mitigate overfitting and ensured that the model remains efficient. Comparisons among other models showed that the proposed model outperformed those with an average train and test accuracy of 99.66% and 99.42%, with a precision, recall, and F1 score of approximately 99.42%. The model was deployed on an edge device (Nvidia Jetson Nano) using a custom developed GUI app and predicted from both saved and real-time data with high confidence values. Grad-CAM visualization was used to identify and represent image areas that affect the convolutional neural network (CNN) classification decision-making process with high accuracy. This research contributes to the development of plant disease classification technologies for edge devices, which have the potential to enhance the ability of autonomous farming for farmers, agronomists, and researchers to monitor and mitigate plant diseases efficiently and effectively, with a positive impact on global food security.

RNDDNet: A residual nested dilated DenseNet based deep-learning model for chilli plant disease classification

The most significant peril to food safety arises from plant diseases, capable of substantially diminishing both the quantity and quality of agricultural yields. Identifying these plant diseases stands out as the foremost challenge within the agricultural sector. Convolutional and deep neural networks prove effective in resolving image classification challenges within the realm of computer vision. Numerous Deep Neural Network(DNN)-based structures have been employed to diagnose plant diseases. Many DNN models in the field make use of various iterations of Dense and DenseNet layers in order to enhance the receptive field and capture intricate features within the data. However, it is important to note that such models often come with a significant computational burden and can introduce aliasing artifacts due to their complexity and resource-intensive nature. To overcome those limitations, we proposed a novel Residual Nested Dilated DenseNet based deep-learning (RNDDNet) model in this paper. Residual Nested Dilated DenseNet model residual connections are achieving the required receptive field, and their dilation factors are effective in extracting more features. The RNDDNet model exhibits the highest level of accuracy in identifying plant diseases. This research introduces a less computational cost and compact model to detect diseases in plant leaves. The proposed model functions to identify diseases, utilizing a dataset comprising 3,800 photographs of chilli leaves, categorized into six distinct classes: five disorder classes and one healthy chilli class. Through experimentation, the outcomes indicate that the suggested model achieves an accuracy of 98.09 %, along with a precision of 97 %, a recall of 97.25 %, and an F1 score of 97.25%. The presented approach demonstrates its superiority over existing methodologies.

Tenfold Enhancement of Wear Resistance by Electrosynthesis of a Nanostructured Self‐Lubricating Al2O3/Sn(S)MoS2 Composite Film on AlSiCu Casting Alloys

Enhancing tribological performance through nanostructure control is crucial for saving energy and improving wear resistance for diverse applications. We introduce a new electrochemical approach that integrates aluminum (Al) anodization, tin alternating current (AC) electrodeposition, and anodic MoS2 electrosynthesis for fabricating nanostructured Al2O3/Sn(S)MoS2 composite films on AlSiCu casting alloys. Our unique process uses Sn‐modified MoS2 deposition to form robust solid lubricant MoS2–SnS electrodeposits within the nanochannels and microsized voids/defects of anodic alumina matrix films on the base materials, resulting in a bilayered Al2O3/SnSMoS2 and MoS2–SnS–Sn composite film. The AC‐deposited Sn enhances conductivity in the anodic alumina matrix film, acts as catalytic nuclei for Sn@SnS@MoS2 core‐shell nanoparticles and a dense top layer, and serves as a reductant for the direct synthesis of hybrid solid lubricant MoS2–SnS from MoS3 by anodic electrolysis of MoS42− ions. The resulting nanocomposite film provides a two‐fold increase in lubricity (friction coefficient (COF) μ = 0.14 ⇒ 0.07) and a ten‐fold improvement in wear resistance (COF μ < 0.2) compared to conventional Al2O3/MoS2 film formed by anodizing and reanodizing. The effectiveness of the Al2O3/Sn(S)MoS2 composite is further validated through real automotive engine piston tests.

Impairment of the GABAergic system in the anterior insular cortex of heroin-addicted males.

Opioid addiction is a global problem, causing the greatest health burden among drug use disorders, with opioid overdose deaths topping the statistics of fatal overdoses. The multifunctional anterior insular cortex (AIC) is involved in inhibitory control, which is severely impaired in opioid addiction. GABAergic interneurons shape the output of the AIC, where abnormalities have been reported in individuals addicted to opioids. In these neurons, glutamate decarboxylase (GAD) with its isoforms GAD 65 and 67 is a key enzyme in the synthesis of GABA, and research data point to a dysregulation of GABAergic activity in the AIC in opioid addiction. Our study, which was performed on paraffin-embedded brains from the Magdeburg Brain Bank, aimed to investigate abnormalities in the GABAergic function of the AIC in opioid addiction by densitometric evaluation of GAD 65/67-immunostained neuropil. The study showed bilaterally increased neuropil density in layers III and V in 13 male heroin-addicted males compared to 12 healthy controls, with significant U-test P values for layer V bilaterally. Analysis of confounding variables showed that age, brain volume and duration of formalin fixation did not confound the results. Our findings suggest a dysregulation of GABAergic activity in the AIC in opioid addiction, which is consistent with experimental data from animal models and human neuroimaging studies.

Utilizing Deep Feature Fusion for Automatic Leukemia Classification: An Internet of Medical Things-Enabled Deep Learning Framework.

Acute lymphoblastic leukemia, commonly referred to as ALL, is a type of cancer that can affect both the blood and the bone marrow. The process of diagnosis is a difficult one since it often calls for specialist testing, such as blood tests, bone marrow aspiration, and biopsy, all of which are highly time-consuming and expensive. It is essential to obtain an early diagnosis of ALL in order to start therapy in a timely and suitable manner. In recent medical diagnostics, substantial progress has been achieved through the integration of artificial intelligence (AI) and Internet of Things (IoT) devices. Our proposal introduces a new AI-based Internet of Medical Things (IoMT) framework designed to automatically identify leukemia from peripheral blood smear (PBS) images. In this study, we present a novel deep learning-based fusion model to detect ALL types of leukemia. The system seamlessly delivers the diagnostic reports to the centralized database, inclusive of patient-specific devices. After collecting blood samples from the hospital, the PBS images are transmitted to the cloud server through a WiFi-enabled microscopic device. In the cloud server, a new fusion model that is capable of classifying ALL from PBS images is configured. The fusion model is trained using a dataset including 6512 original and segmented images from 89 individuals. Two input channels are used for the purpose of feature extraction in the fusion model. These channels include both the original and the segmented images. VGG16 is responsible for extracting features from the original images, whereas DenseNet-121 is responsible for extracting features from the segmented images. The two output features are merged together, and dense layers are used for the categorization of leukemia. The fusion model that has been suggested obtains an accuracy of 99.89%, a precision of 99.80%, and a recall of 99.72%, which places it in an excellent position for the categorization of leukemia. The proposed model outperformed several state-of-the-art Convolutional Neural Network (CNN) models in terms of performance. Consequently, this proposed model has the potential to save lives and effort. For a more comprehensive simulation of the entire methodology, a web application (Beta Version) has been developed in this study. This application is designed to determine the presence or absence of leukemia in individuals. The findings of this study hold significant potential for application in biomedical research, particularly in enhancing the accuracy of computer-aided leukemia detection.

Utilizing Deep Feature Fusion for Automatic Leukemia Classification: An Internet of Medical Things-Enabled Deep Learning Framework

Acute lymphoblastic leukemia, commonly referred to as ALL, is a type of cancer that can affect both the blood and the bone marrow. The process of diagnosis is a difficult one since it often calls for specialist testing, such as blood tests, bone marrow aspiration, and biopsy, all of which are highly time-consuming and expensive. It is essential to obtain an early diagnosis of ALL in order to start therapy in a timely and suitable manner. In recent medical diagnostics, substantial progress has been achieved through the integration of artificial intelligence (AI) and Internet of Things (IoT) devices. Our proposal introduces a new AI-based Internet of Medical Things (IoMT) framework designed to automatically identify leukemia from peripheral blood smear (PBS) images. In this study, we present a novel deep learning-based fusion model to detect ALL types of leukemia. The system seamlessly delivers the diagnostic reports to the centralized database, inclusive of patient-specific devices. After collecting blood samples from the hospital, the PBS images are transmitted to the cloud server through a WiFi-enabled microscopic device. In the cloud server, a new fusion model that is capable of classifying ALL from PBS images is configured. The fusion model is trained using a dataset including 6512 original and segmented images from 89 individuals. Two input channels are used for the purpose of feature extraction in the fusion model. These channels include both the original and the segmented images. VGG16 is responsible for extracting features from the original images, whereas DenseNet-121 is responsible for extracting features from the segmented images. The two output features are merged together, and dense layers are used for the categorization of leukemia. The fusion model that has been suggested obtains an accuracy of 99.89%, a precision of 99.80%, and a recall of 99.72%, which places it in an excellent position for the categorization of leukemia. The proposed model outperformed several state-of-the-art Convolutional Neural Network (CNN) models in terms of performance. Consequently, this proposed model has the potential to save lives and effort. For a more comprehensive simulation of the entire methodology, a web application (Beta Version) has been developed in this study. This application is designed to determine the presence or absence of leukemia in individuals. The findings of this study hold significant potential for application in biomedical research, particularly in enhancing the accuracy of computer-aided leukemia detection.

Real-time confinement regime detection in fusion plasmas with convolutional neural networks and high-bandwidth edge fluctuation measurements

Abstract A real-time detection of the plasma confinement regime can enable new advanced plasma control capabilities for both the access to and sustainment of enhanced confinement regimes in fusion devices. For example, a real-time indication of the confinement regime can facilitate transition to the high-performing wide pedestal quiescent H-mode, or avoid unwanted transitions to lower confinement regimes that may induce plasma termination. To demonstrate real-time confinement regime detection, we use the 2D beam emission spectroscopy (BES) diagnostic system to capture localized density fluctuations of long wavelength turbulent modes in the edge region at a 1 MHz sampling rate. BES data from 330 discharges in either L-mode, H-mode, Quiescent H (QH)-mode, or wide-pedestal QH-mode was collected from the DIII-D tokamak and curated to develop a high-quality database to train a deep-learning classification model for real-time confinement detection. We utilize the 6x8 spatial configuration with a time window of 1024 $\mu$s and recast the input to obtain spectral-like features via FFT preprocessing. We employ a shallow 3D convolutional neural network for the multivariate time-series classification task and utilize a softmax in the final dense layer to retrieve a probability distribution over the different confinement regimes. Our model classifies the global confinement state on 44 unseen test discharges with an average $F_1$ score of 0.94, using only $\sim$1 millisecond snippets of BES data at a time. This activity demonstrates the feasibility for real-time data analysis of fluctuation diagnostics in future devices such as ITER, where the need for reliable and advanced plasma control is urgent.

Monitoring and assessing the effectiveness of the biological control implemented to address the invasion of water hyacinth (Eichhornia crassipes) in Hartbeespoort Dam, South Africa

Water hyacinth is one of the most aggressive alien invasive plants, which invades freshwater resources and destroys native biodiversity. The plant proliferates rapidly over a short space of time, forming thick dense layer on the surface of freshwater bodies. Monitoring and management of water hyacinth is essential to protect water resources affected by the presence of this plant. The study assessed the effectiveness of biological agent (Megamelus scutellaris) applied in the Hartbeespoort Dam from pre (2016–2017) and post (2018–2023) biological control to manage water hyacinth spread and proliferation. In achieving this main goal, the study used advanced cloud-computing machine learning techniques and multi date Sentinel-2 Multispectral Instrument (MSI) data to monitor the effectiveness of such biological control. During this analysis, remote sensing data was acquired for two time periods namely: pre-intervention (2016–2017) and post intervention (2018–2023) to establish variation in the spatio-temporal distribution of water hyacinth in the Hartbeespoort Dam using various machine learning techniques (Support Vector Machine (SVM), Classification and Regression Tree (CART), Random Forest (RF) and Naïve Bayes (NB)) in Google Earth Engine cloud computing platform, and assessed the spectral separability of water hyacinth from numerous land cover types, within and around the Hartbeespoort Dam using the Sentinel-2 derived spectral reflectance curves. The results indicated that the extent of water hyacinth area coverage decreased from 15% to below 6% between the period of 2018 and 2021, however, a significant increase was noted between November 2022 and April 2023, after the biological control was introduced. The significant increase observed during the time period of November 2022 and April 2023 can be attributed to nutrient rich water discharging into the dam from the Crocodile River during the time of flooding reported in November 2022. The result further indicate that RF produced the highest overall accuracies ranging between 93.42% and 98.70%. While NB produced the lowest accuracies ranging between 87.76% and 92.08%. These findings underscore the relevance of new generation satellite dataset and machine learning algorithms in monitoring the effectiveness of the biological controls of alien invasive spread provide information regarding alien plant invasion. Therefore, aligning with Sustainable Development Goals (SDG 6) emphasizing on the importance of implementing effective control measures to control invasive species and their impact on water resources thus ensuring the sustainability of freshwater ecosystems and the availability of clean water resources.

Electronic Properties of Group-III Nitride Semiconductors and Device Structures Probed by THz Optical Hall Effect.

Group-III nitrides have transformed solid-state lighting and are strategically positioned to revolutionize high-power and high-frequency electronics. To drive this development forward, a deep understanding of fundamental material properties, such as charge carrier behavior, is essential and can also unveil new and unforeseen applications. This underscores the necessity for novel characterization tools to study group-III nitride materials and devices. The optical Hall effect (OHE) emerges as a contactless method for exploring the transport and electronic properties of semiconductor materials, simultaneously offering insights into their dielectric function. This non-destructive technique employs spectroscopic ellipsometry at long wavelengths in the presence of a magnetic field and provides quantitative information on the charge carrier density, sign, mobility, and effective mass of individual layers in multilayer structures and bulk materials. In this paper, we explore the use of terahertz (THz) OHE to study the charge carrier properties in group-III nitride heterostructures and bulk material. Examples include graded AlGaN channel high-electron-mobility transistor (HEMT) structures for high-linearity devices, highlighting the different grading profiles and their impact on the two-dimensional electron gas (2DEG) properties. Next, we demonstrate the sensitivity of the THz OHE to distinguish the 2DEG anisotropic mobility parameters in N-polar GaN/AlGaN HEMTs and show that this anisotropy is induced by the step-like surface morphology. Finally, we present the temperature-dependent results on the charge carrier properties of 2DEG and bulk electrons in GaN with a focus on the effective mass parameter and review the effective mass parameters reported in the literature. These studies showcase the capabilities of the THz OHE for advancing the understanding and development of group-III materials and devices.

Characterization of microstructure and oxidation behavior of Al modified MoSi2 coating on Nb-Si based alloy

The aim of this work is to investigate the effects of Al on the microstructure formation and oxidation behavior of MoSi2 coating on Nb-Si based alloy by slurry sintering. The results indicate that the addition of a small amount of Al has little effect on the formation of MoSi2 coating. The phase constituent and structure of the inter-diffusion zone (IDZ) at the interface change significantly with the increase of Al content. The Al2O3 formed during the coating preparation process can hinder the diffusion of Si and Fe, thus suppressing formation of Mo5Si3 and Nb4Fe3Si5 layers in IDZ of the coatings with higher Al, but instead forming Fe-rich (Ti,Nb)5Si4 layer and (Nb,X)5Si3 layer. The higher amount of Al2O3 will increase the sintering resistance between the Mo and Si, resulting in a high porosity of the MoSi2 coating. By comparison, the 0.1Al coating exhibits the best oxidation resistance at 1250 °C. The preferential oxidation of Al promotes the formation of SiO2, which is conducive to the rapid filling of pores in the lower part of MoSi2 coating, forming a dense mixed layer of MoSi2+Mo5Si3+SiO2. However, adding a higher content of Al reduces the oxidation resistance of the coating.

Optimizing P3HT/PCBM-Based Organic Photodetector Performance: Insights from SCAPS 1D Simulation Studies.

Organic electronics have great potential due to their flexible structure, high performance, and their ability to build effective and low-cost photodetectors. We investigated the parameters of the P3HT and PCBM layers for device performance and optimization. SCAPS-1D simulations were employed to optimize the thicknesses of the P3HT and PCBM layers, investigate the effects of shallow doping in the P3HT layer, and assess the influence of the back contact electrode's work function on device performance. Furthermore, this study explored the impact of interface defect layer density on the characteristics of the device. Through systematic analyses, the optimal parameters for enhancing device responsivity were identified. The findings indicate that a P3HT layer thickness of 1200 nm, a PCBM layer thickness of 20 nm, and a back contact electrode with a work function of 4.9 eV achieve the highest responsivity. Notably, at a bias of -0.5 V, the responsivity exceeds 0.4 A/W within the wavelength range of 450 nm to 630 nm. These optimized parameters underscore the significant potential of the developed device as an organic photodetector, particularly for visible light detection.