Demand For Applications Research Articles

Advanced battery management system enhancement using IoT and ML for predicting remaining useful life in Li-ion batteries

This study highlights the increasing demand for battery-operated applications, particularly electric vehicles (EVs), necessitating the development of more efficient Battery Management Systems (BMS), particularly lithium-ion (Li-ion) batteries used in energy storage systems (ESS). This research addresses some of the key limitations of current BMS technologies, with a focus on accurately predicting the remaining useful life (RUL) of batteries, which is a critical factor for ensuring operational efficiency and sustainability. Real-time data are collected from sensors via an Internet of Things (IoT) device and processed using Arduino Nano, which extracts values for input into a Long Short-Term Memory (LSTM) model. This model employs the National Aeronautics and Space Administration (NASA) Li-battery dataset and current, voltage temperature, and cycle values to predict the battery RUL. The proposed model demonstrates significant forecasting precision, attaining a root mean square error (RMSE) of 0.01173, outperforming all comparative models. This improvement facilitates more effective decision-making in BMS, particularly in resource allocation and adaptability to transient conditions. However, the practical implementation of real-time data acquisition systems at a scale and across diverse environments remains challenging. Future research will focus on enhancing the generalizability of the model, expanding its applicability to broader datasets, and automating data ingestion to minimize integration challenges. These advancements are aimed at improving energy efficiency in both industrial and residential applications in accordance with the Sustainable Development Goals (SDGs) of the UN.

Web Development Framework

Web development frameworks are essential to contemporary web development because they provide an organized method for creating dynamic, feature-rich, and responsive websites. This Research paper delves into the intricacies of web development framework, exploring their significance, functionality, and the diverse landscape they inhabit. The paper further investigates various types of frameworks, including front-end, back-end, and full-stack frameworks, shedding light on their distinct features and suitability for different project requirements. Finally, the paper concludes with a discussion on the feature trends and challenges in web development framework, including the rise of serverless architectures and the increasing demand for real-time applications. By synthesizing current research and industry insights, this paper aims to provide a comprehensive understanding of web development and their impact on modern web development practices

In Situ Generation of Microwire Heterojunctions with Flexible Optical Waveguide and Hydration‐Mediated Photochromism

AbstractFlexible heterojunctions based on molecular systems are in high demand for applications in photonics, electronics, and smart materials, but fabrication challenges have hindered progress. Herein, we present an in situ approach to creating optical heterojunctions using hydration‐mediated flexible molecular crystals. These hydrated multi‐component molecular solids display strong blue emitting optical waveguides with minimal optical loss (0.005 dB/μm) and excellent flexibility (elastic modulus: 3.87 GPa). The water‐mediated process enables the molecular microwires with tunable elastic and plastic deformation, as well as reversible uptake and release of lattice water, facilitating the formation of flexible heterojunctions. Spectral analysis and theoretical modeling reveal that these microwires exhibit both photochromism and color‐tunable dual emission (fluorescence and phosphorescence), expanding their utility in photonic information encoding. Therefore, this work introduces a hydration‐mediated molecular engineering strategy for fabricating crystalline heterojunctions with on‐demand processability and controllable emission sequences, enabling optical signal manipulation at the micro/nanoscale.

Optimization of Delay and Area for Approximate Radix-8 Booth Multiplier

This paper investigates the optimization of Radix-8 Booth Multipliers, which are essential for efficient arithmetic operations in modern digital systems, particularly in applications such as digital signal processing, telecommunications, and image processing where rapid and accurate calculations are crucial. The study aims to enhance performance by focusing on reducing both delay and area while ensuring that acceptable accuracy levels are maintained for error-tolerant applications. To achieve these optimization goals, we compare three methodologies: the Carry Save Adder (CSA), the Kogge Stone Adder (KSA), and the Carry Look-Ahead Adder (CLA), each of which presents unique advantages and trade-offs in terms of speed, area utilization, and power consumption. The results of our analysis demonstrate that the Kogge Stone Adder provides the best overall performance in terms of speed and area efficiency, making it the most suitable choice for optimizing Radix-8 Booth Multipliers, particularly in scenarios where high performance and efficient resource use are critical. By emphasizing these findings, this study contributes valuable insights into the design of more efficient multipliers that can meet the increasing demands of contemporary digital applications. Key Words: Radix-8 Booth Multiplier, approximate computing, delay optimization, area optimization, Carry Save Adder (CSA), Kogge Stone Adder (KSA), Carry Look-Ahead Adder (CLA), digital systems, arithmetic operations, performance enhancement, error-tolerant applications, partial product generation, hardware description languages (HDLs), digital signal processing, machine learning, approximation techniques, simulation and testing, power consumption, ASIC design.

Metagenomic insights into microbial mechanism of pH shifts enhancing short-chain carboxylic acid production from fruit waste anaerobic fermentation

Short-chain carboxylic acids (SCCAs) production from waste-streams is attractive due to the increasing market demand and wide range of applications. This study investigated the influence of pH on the performance of anaerobic SCCA-producing reactors treating fruit waste and employed metagenomics to unveil the shifts and metabolic pathways of functional acidogens. Results showed that high SCCA concentrations of 45.0–65.8 g chemical oxygen demand (COD)/L were maintained at each pH. The maximum concentrations of SCCAs obtained at pH 8 (63.8 g COD/L) under alkaline conditions and at pH 5 (65.8 g COD/L) under acidic conditions were similar, yet there was a pronounced effect of pH on SCCA distribution as acetate and butyrate were the primary products at pH 6, while lactate was dominant at pH 8–10 and at pH 5–4. Key populations involved in acidogenesis at different pHs included Bifidobacterium (acetate fermenter at pH 6–7), Corynebacterium (acetate and lactate fermenter at pH 8–10), Acidipropionibacterium (propionate fermenter at pH 7–10), Clostridium (acetate, propionate, and butyrate fermenter at pH 6–5), and Lactobacillus (lactate fermenter at pH 5). This study enhances our understanding of the underlying microbial mechanism governing SCCA production from fruit waste with pH regulation, and provides guidance for the development of production processes for waste-based metabolites useful for the chemical industry.

An Evaluation of the Damping Characteristics of Some Silicon Rubber/Ferric Oxide Composites

ABSTRACTGenerally, polymeric materials with superior damping characteristics are in great demand for many advanced functional applications, such as in building, aerospace, electronics, and transport sectors. However, it still remains a challenge to improve the damping characteristics of such materials owing to the lack of adequate practical knowledge in this area. Here, we report on the successful fabrication of silicon rubber (SR)/ferric oxide (Fe3O4) composites with excellent damping characteristics. The results obtained from the study showed that the damping characteristics of the specimens increased with the loading of Fe3O4 within a range of −105°C to 200°C. Specifically, the damping factor (tanδ) is 0.45 at room temperature, and the effective working temperature, the temperatures that correspond to the tanδ > 0.3, ranged from −105°C to 165°C for the loading of Fe3O4 at 60 phr. The scanning electron microscopy results showed that the excellent damping characteristics of composites can be attributed to the good dispersion of Fe3O4 in the SR matrix. This work, therefore, opens up a pathway toward a relatively easy and up‐scalable means for fabricating polymer composites that have superior damping characteristics and ones with a wider applicability.

From discovery to application: Enabling technology-based optimizing carbonyl reductases biocatalysis for active pharmaceutical ingredient synthesis.

The catalytic conversion of chiral alcohols and corresponding carbonyl compounds by carbonyl reductases (alcohol dehydrogenases), which are NAD(P) or NAD(P)H-dependent oxidoreductases, has attracted considerable attention. However, existing carbonyl reductases are insufficient to meet the demands of diverse industrial applications; hence, new enzymes with functions that can expand the toolbox of biocatalysts are urgently required. Developing precisely controlled chiral biocatalysts is of great significance for the efficient development of a broad spectrum of active pharmaceutical ingredients via biosynthesis. In this review, we summarized methods for discovering novel natural carbonyl reductases from various perspectives. Furthermore, advances in protein engineering, utilizing known sequence and structural information as well as catalytic dynamics mechanisms to improve potential functions, are also addressed. The exponential growth in data-driven tools over the past decade has made it possible to de novo design carbonyl reductases. Additionally, various applications of these high-performance carbonyl reductases and different strategies for coenzyme regeneration involving photocatalysis during the reaction process were reviewed. These advancements will bring new opportunities and challenges to the fields of green chemistry and biosynthesis in the future.

RT-SVM: Channel modeling and analysis for indoor terahertz communication scenarios

Considering the increasing demands for wireless communication networks and information system applications, the wireless sector must meet the pressing requirement for high-speed technological advances. The terahertz (THz) frequency band, spanning 0.3 to 10 THz, is of significant interest in current technological innovations and academic research in telecommunications. The THz frequency band has unique properties, including high time-resolving power (femtosecond) and low absorption. This paper proposes a THz propagation ultra-wideband (UWB) channel model and coding scheme for indoor environments starting from 0.3 THz. First, we investigated the propagation path loss model by considering the effects of transmitter dimensions, molecular absorption, and attenuation as functions of frequency and distance. We developed models for power propagation delay, multiple input multiple output (MIMO) systems and discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM) response channels. Using the standard Saleh-Valenzuela model combined with Ray-tracing (RT-SVM), we studied the transmission of THz signals in indoor scenarios. We introduced physical parameters relevant to the THz indoor channel, such as line-of-sight (LoS) path loss, power distributions, temporal and spatial properties, and associations between THz multipath properties. These parameters were integrated with the RT-SVM channel model and applied to THz indoor communication. Numerical simulations demonstrate that the proposed hybrid channel model enhances THz system performance and outperforms traditional statistical and geometric-based stochastic channel models in terms of temporal and spatial dimensions, contributing to frequency loss variations.

Beetroot Microgreens (Beta vulgaris) Powder Obtained by Cast‐Tape Drying and Freeze‐Drying

ABSTRACTThe study aimed to dehydrate beet microgreen foams using cast‐tape drying and freeze‐drying to obtain powder and characterize them in terms of physicochemical properties and betalain. Foam formulations were analyzed: F1 = 78.3% water + 13% microgreens + 6.5% Emustab + 2.2% pre‐gelatinized starch; F2 = 78.3% water + 13% microgreens + 4.4% pre‐gelatinized starch + 4.3% Emustab. The formulated foams were dried using cast‐tape drying (CTD) (70°C and 2 mm spreading thickness) and freeze‐dried (FD). The drying time for CTD was 12 min for CTDF1 and 24 min for CTDF2 (moisture of 0.1 g/g). The FD time was 14 h for FDF1 and 18 h for FDF2 (moisture of 0.02 g/g). The water activity and moisture content of the powder obtained by CTD were lower than that of the FD powder for both formulations, and the protein content in F2 (4%–4.6%) was lower than in F1 (5.2%–6.3%). The foam allows for the rapid drying of beet microgreens by CTD due to the high drying rate and moderate temperature. The CTDF2 powder has lower moisture and activity water, violet/red coloration, good flowability, and low cohesiveness compared to the other samples. Freeze‐drying shows higher betalain retention (FDF1). Microgreens in powdered form constitute viable protein sources to be incorporated into plant‐based products, while betalain allows using this powder as a natural dye. These potential applications demand further investigation, aiming at a potential introduction as a new product in the market.

Brain-phenotype predictions of language and executive function can survive across diverse real-world data: Dataset shifts in developmental populations

Predictive modeling potentially increases the reproducibility and generalizability of neuroimaging brain-phenotype associations. Yet, the evaluation of a model in another dataset is underutilized. Among studies that undertake external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies (i.e., dataset shifts). Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized developmental samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. Through advanced methodological approaches, we demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features. Results indicate the potential of functional connectome-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of brain-phenotype associations in real-world scenarios and clinical settings.

Cloud data centers and networks: Applications and optimization techniques

As the volume and complexity of big data continue to escalate, optimizing the performance, scalability, and energy efficiency of big data applications within cloud data centers has become increasingly crucial. This journal presents a comprehensive survey of current optimization techniques, focusing on data placement, job scheduling, and network configurations tailored for cloud environments. We explore the impact of various data center topologies on the performance of big data frameworks like Hadoop, emphasizing the trade-offs between performance and energy efficiency. Advanced methodologies, including dynamic data placement strategies, locality-aware scheduling, and innovative reduce task placement techniques, are reviewed in depth. Additionally, we highlight the importance of network power effectiveness (NPE) and examine the role of optical and electronic switching technologies in enhancing data center efficiency. By synthesizing findings from recent studies, this paper provides valuable insights into the optimization of cloud data centers, offering recommendations for improving resource utilization and reducing job completion times while maintaining energy efficiency. The findings contribute to the ongoing efforts to scale and adapt cloud data infrastructures for the rapidly growing demands of big data applications.

Air-Cooled Heat Exchangers as a Heat Sink: Heat Transfer

This paper discusses heat transfer concepts in air-cooled heat exchangers (ACHEs) and how ACHEs work like the principle of Heat Sink. A heat exchanger used for transferring heat between source and a working fluid. There use is for cooling and heating processes and have a huge impact on different industries coolers are an environmentally friendly solution as they do not require an additional water supply due to water loss from drift and evaporation, making them suitable for locations where water as a cooling medium is not readily available . These devices utilize air as a cooling medium, transferring heat from a hot source to the environment. As technology advances and demands for higher performance and energy efficiency increase, research on air-cooled heat exchangers continues to evolve. This research area offers significant opportunities to contribute to advancements in various fields, improve energy efficiency, and address environmental concerns. By exploring innovative design approaches and leveraging emerging technologies, researchers can develop air-cooled heat exchangers that meet the evolving demands of modern applications

Vacuum suction end-effector development for robotic harvesters of fresh market apples

Timely harvesting of fresh apples faces challenges due to labour shortage, and the modern approach of robotic harvesting has the potential to address this issue. The prevailing process of apple harvest robotics could not meet the demands of practical applications, mainly due to the lack of a suitable manipulator, because the existing ones are associated with low picking rates, fruit damage, and high costs. A prototype apple harvesting manipulator was developed, which includes a vacuum three-revolute-degrees-of-freedom end-effector, a three-prismatic-degrees-of-freedom Cartesian system, an RGB-D camera, and system integration. The vision positioning system and controller were designed to realise precise positioning and detachment of the manipulator. The major contribution of the current study is the three-revolute-degrees-of-freedom vacuum suction end-effector, whose performance evaluation was conducted in a commercial apple orchard. Experimental results showed that a 33ϕ mm diameter suction cup achieved superior performance over a 43ϕ mm cup. The method of rotation followed by pull proved to be more effective than only pulling for apple detachment. The results indicated that the apple’s equatorial region was the optimal area for suction. Furthermore, the vacuum pressure should be at least −65 kPa to guarantee successful detachment. Experimental results showed that 83.1% of harvested apples had stems intact. For the developed manipulator, a 33ϕ mm diameter suction cup, a rotate-and-pull separation method, and −65 kPa were recommended for practical applications. With the integrated new manipulator, the developed apple harvest robot has been demonstrated to have the potential to realise robotic apple harvesting.

In Situ Generation of Microwire Heterojunctions with Flexible Optical Waveguide and Hydration-Mediated Photochromism.

Flexible heterojunctions based on molecular systems are in high demand for applications in photonics, electronics, and smart materials, but fabrication challenges have hindered progress. Herein, we present an in situ approach to creating optical heterojunctions using hydration-mediated flexible molecular crystals. These hydrated multi-component molecular solids display strong blue emitting optical waveguides with minimal optical loss (0.005 dB/μm) and excellent flexibility (elastic modulus: 3.87 GPa). The water-mediated process enables the molecular microwires with tunable elastic and plastic deformation, as well as reversible uptake and release of lattice water, facilitating the formation of flexible heterojunctions. Spectral analysis and theoretical modeling reveal that these microwires exhibit both photochromism and color-tunable dual emission (fluorescence and phosphorescence), expanding their utility in photonic information encoding. Therefore, this work introduces a hydration-mediated molecular engineering strategy for fabricating crystalline heterojunctions with on-demand processability and controllable emission sequences, enabling optical signal manipulation at the micro/nanoscale.

Exploring the wear mechanism of surface hardfacing Stellite 6 alloy on the cast iron over the wide temperature range

Abstract Cast iron is widely used as a heat-resistant material in the automotive industry, but it increasingly struggles to meet the demands of high-temperature friction applications. This study employs surface hardfacing with Stellite 6 alloy to improve the wear resistance of cast iron. By conducting sliding friction tests at various temperatures, we systematically investigate the friction behavior of both cast iron and hardfacing specimens over a broad temperature range. The results reveal that hardfacing-treated specimens exhibit exceptional wear resistance. At ambient temperatures, the hardfacing specimen shows a 65.5% reduction in wear loss compared to the cast iron specimen. This reduction increases to 83.8% at temperatures up to 600 °C. At room temperature, the wear mechanisms of cast iron include abrasive wear and fatigue wear. At medium temperatures, abrasive wear is the primary mechanism of cast iron. At high temperatures, the wear mechanisms of cast iron consist of abrasive wear, oxidative wear, and adhesive wear. In contrast, the wear mechanisms of the hardfacing specimens differ: at room and medium temperatures, abrasive wear is the predominant mechanism, while at high temperatures, the main mechanisms are abrasive wear and adhesive wear. The superior mechanical properties and enhanced resistance to high-temperature oxidation of the hardfacing specimens are the primary factors contributing to their improved friction performance across the temperature spectrum.

Characterization and Processing of Low-Grade Middle Group 2 Chromite Ore by Gravity Shaking Table and a Comparative SLon Magnetic Separation: A Case Study

Chromite is considered a strategic mineral in the global economy. It is mainly used as an essential raw material in the production of stainless steel and other metal alloys due to its corrosion and heat resistance properties. High-grade chromite resources are gradually depleting; with the increasing chromite demand in metallurgical applications, studies have focused on exploring low-grade and alternative chromite sources. This study proposes a cost-effective processing flowsheet for the low-grade middle group 2 (MG2) chromite layer, a poorly explored chromatite seam within the South African bushveld igneous complex (BIC). The study involved mineralogical characterization followed by gravity and magnetic separation of the low-grade MG2 ore at 18.18% Cr2O3. Characterization by XRD and Auto-SEM revealed that the ore mainly consists of pyroxene, chromite, and feldspar, with other minerals in trace quantities. The gravity separation test by shaking table upgraded the chromite (Cr2O3) to 42.0% at high chromite recoveries, whereas the laboratory Slon wet high-intensity magnetic separation method (SLon WHIMS) upgraded the chromite in the feed to 42.95% grade at lower chromite recoveries. Desliming the sample before the gravity and magnetic separation tests significantly improved the separation. The magnetic separation tests further demonstrated that chromite within the MG2 layer is sensitive to magnetic separation due to its high iron content. The adapted flowsheet is proposed as a cost-effective flowsheet for processing the low-grade MG2 layer. The flow sheet can be optimized by conducting the SLon WHIMS tests at high intensities followed by fine gravity tests by spiral circuits to maximize the chromite recovery while achieving commercial chromite grades and a Cr:Fe ratio greater than 1.5.

Lightweight video object segmentation: Integrating online knowledge distillation for fast segmentation

The typical shortcoming of STM (Space-Time Memory Network) mode video object segmentation algorithms is their high segmentation performance coupled with slow processing speeds, which poses challenges in meeting real-world application demands. In this work, we propose using an online knowledge distillation method to develop a lightweight video segmentation algorithm based on the STM mode, achieving fast segmentation while maintaining performance. Specifically, we utilize a novel adaptive learning rate to tackle the issue of inverse learning during distillation. Subsequently, we introduce a Smooth Block mechanism to reduce the impact of structural disparities between the teacher and student models on distillation outcomes. Moreover, to reduce the fitting difficulty of the student model on single-frame features, we design the Space-Time Feature Fusion (STFF) module to provide appearance and position priors for the feature fitting process of each frame. Finally, we employ a simple Discriminator module for adversarial training with the student model, to encourage the student model to learn the feature distribution of the teacher model. Extensive experiments show that our algorithm attains performance comparable to the current state-of-the-art on both DAVIS and YouTube datasets, despite running up to ×4 faster, with ×20 fewer parameters and ×30 fewer GFLOPS.

Soft Actor-critic-based Distributed Routing Scheme for Edge Computing Integrated with Dynamic IoT Networks

The rapid expansion of the Internet of Things (IoT) necessitates advanced routing schemes capable of meeting the stringent demands for low latency and high accuracy, which are critical for applications such as autonomous vehicles and telemedicine. Traditional edge computing methods often struggle with elevated latency, rendering them unsuitable for time-sensitive applications. Additionally, many reinforcement learning (RL) algorithms require action space discretization, which can introduce biases and dimensionality challenges. This paper introduces a novel Soft Actor-Critic (SAC)-based distributed routing scheme for edge computing, specifically designed to address these limitations. By integrating RL with Maximum Entropy principles and employing a decentralized approach, the proposed model enhances network performance, reduces delays, and effectively manages multi-optimality criteria. The distributed routing scheme operates independently of a centralized controller, allowing routers to make autonomous decisions and adapt seamlessly to changes in the network. This is accomplished through a Markov Decision Process (MDP) that optimizes routing paths based on various factors, including node depth, energy consumption, and transmission probability. The methodology encompasses local training phases for individual nodes, followed by federated training to refine the model across the network. Experimental results conducted on topologies of varying scales demonstrate the model’s efficacy in achieving high accuracy and efficient convergence, particularly in dynamic IoT environments. These findings underscore the potential of the proposed SAC-based distributed routing scheme as a robust solution for enhancing routing efficiency and reliability in the evolving landscape of IoT applications. IMPACT STATEMENT The rapid expansion of Internet of Things (IoT) applications demands advanced routing solutions to ensure low latency and high accuracy, crucial for sectors like autonomous vehicles and telemedicine. Traditional edge computing methods struggle with elevated latency, while many reinforcement learning (RL) algorithms face challenges with action space discretization, leading to biases and dimensionality issues. This study introduces a novel Soft Actor-Critic (SAC)-based distributed routing scheme to address these limitations. Integrating Maximum Entropy principles with RL enhances exploration and decision-making stability. The decentralized approach allows routers to make autonomous, real-time decisions based on local network conditions, optimizing routing paths through a Markov Decision Process (MDP). Experimental results from various simulated IoT network topologies show the model’s superior performance in reducing delays and maintaining bandwidth. This research paves the way for more reliable, low-latency IoT applications, significantly enhancing routing efficiency and network adaptability in dynamic IoT environments.

Optimizing Si─O Conjugation to Enhance Interfacial Kinetics for Low-Temperature Rechargeable Lithium-Ion Batteries.

With the growing demand for high-voltage and wide-temperature range applications of lithium-ion batteries (LIBs), the requirements for electrolytes have become increasingly stringent. While fluorination engineering has enhanced the performance of traditional solvent systems, it has also raised concerns regarding cost, environmental hazards, and low reduction stability. Through strategic molecular bond design, a novel class of low-temperature (LT) solvents-siloxanes-is identified, meeting the demands of LT and high-voltage applications in LIBs. The d-p conjugation of the Si─O bond enhances voltage resistance and weakens the Li+-solvent interactions. By modulating the number of Si─O conjugated bonds, the type of anion clusters in the solvation structure can be controlled, ultimately leading to the formation of a LiF and Si─O-rich interfacial layer and facilitating rapid Li+ conduction. Consequently, the graphite||NCM811 pouch cell (2.3 Ah, 4.45 V) with a siloxane-based electrolyte retains 75.1% of room temperature capacity (RTC) at -50°C. The rapid interface kinetics allow a superior reversible charging capacity retention of 67.6% at -40°C, with good cycle stability at -20°C. This study provides new insights into solvent design to fortify LIB performance in harsh conditions.

High-power narrow-linewidth tunable blue laser diode array utilizing a blazed grating external cavity

High-power, narrow-linewidth blue laser sources have been in high demand for applications in laser pumping and spectral beam combining. In this paper, a blue laser source, consisting of 12 transistor-outline (TO) packaged laser diodes (LD), is established through space beam combining. An improved external cavity (EC) utilizing a blazed grating (BG), a beam splitter, and a beam expander is investigated. Through injection feedback and mode competition, a laser output, with 31.2 W power, 445.04 nm central wavelength, 0.18 nm full-width at half maximum (FWHM) linewidth, is achieved at a driving current of 3.0 A. A tunable range of 3.6 nm is observed at 2.0 A driving current. Additionally, the effect of the deformation of the aluminum-coated grating under a high-intensity blue laser is examined. The external cavity requires a moderately efficient blazed grating and prevents potential damage caused by high absorption and thermal stress concentration. The system exhibits excellent temporal stability in both output power and spectrum. Moreover, wavelength-locking experiments using both a volume Bragg grating (VBG) and a surface grating (SG) are conducted to serve as comparative tests for this study. Compared with volume Bragg gratings, blazed gratings offer spectral tunability and are insensitive to temperature perturbations and mechanical stress. Compared with surface gratings, blazed gratings offer a relatively high threshold and stable performance at high driving currents. Furthermore, blazed gratings are more cost-effective than VBGs, providing a competitive advantage. To the best of our knowledge, it’s the first blue laser source with over 30 W output and 0.18 nm FWHM linewidth utilizing a blazed grating external cavity.