Industrial Applications Research Articles

Satellite dish-like nanocomposite as a breakthrough in single photon detection for highly developed optoelectronic applications

A nanocomposite with a unique satellite dish-like structure, termed arsenic (III) oxide iodide (AsO2I)/polypyrrole (Ppy) intercalated with iodide ions (AsO2I/Ppy-I), has been meticulously developed via a two-step process. It features natural satellite dish-like nanostructures, potentially serving as nanoresonators for efficient single photon absorption. With a bandgap of 2.8 eV, the AsO2I/Ppy-I nanocomposite efficiently absorbs photons in the UV and visible light regions, making it suitable for single photon detection. Impressive performance is seen in photocurrent sensitivity measurements, recording values of 0.017 mA.cm−2 under white light and 0.009 mA.cm−2 under monochromatic light at 340 nm. Additionally, it exhibits high responsivity and detectivity, with peak values at wavelengths of 340 nm and 440 nm associated with the diameter of the Satellite dish nanostructure. Cost-effectiveness and simple synthesis methods make it attractive for industrial applications, while its unique structural characteristics and enhanced optical properties position it as a valuable asset in optoelectronic technologies. It holds promise as a leading material in advanced quantum technology, marking a significant leap forward in optoelectronic technologies, with potential applications in quantum cryptography, communication, and computing.

Six Pulse Type Segmented Thyristor Controlled Reactor with Fixed Capacitor for Reactive Power Compensation

This article presents a Six Pulse Type Segmented Thyristor Controlled Reactor (TCR) integrated with a Fixed Capacitor (FC) for reactive power compensation. The primary objective is to improve voltage stability and power factor in electrical networks, addressing issues related to reactive power imbalance and harmonic distortion. The proposed configuration combines the advantages of segmented TCR and FC, providing a flexible and efficient approach to reactive power management. The novelty of this work lies in the segmentation of the TCR, which enhances the dynamic control of reactive power by allowing more precise regulation and reduced harmonics compared to conventional TCR systems. This segmented approach also minimizes switching losses and thermal stress on thyristors, leading to enhanced reliability and longevity of the system. Additionally, the integration of a fixed capacitor optimizes the overall power factor correction, contributing to improved system efficiency. Key findings from simulation and experimental results demonstrate that the Six Pulse Type Segmented TCR with FC significantly reduces reactive power, stabilizes voltage levels, and effectively suppresses harmonics within permissible limits, adhering to IEEE standards. The system shows a marked improvement in power quality, making it a viable solution for industrial applications where reactive power control is critical. This innovative approach not only provides superior compensation characteristics but also offers a scalable and adaptable framework for modern power systems, highlighting its potential to enhance operational performance and energy efficiency in various electrical grids.

Optimizing Electron‐Beam Photothermal Pyrolysis Parameters for Enhanced Molecular Biodegradability of Polyvinyl Chloride Plastics

AbstractPolyvinyl chloride (PVC), a synthetic plastic notable for harmful emissions during deterioration, has potential to be molecularly adjusted by photo‐oxidative ultraviolet degradation. This research investigates the influence of electron beam irradiation and near‐infrared photothermal treatment utilizing gold nanoparticles to establish the optimized molecular weight and beam time exposure for biodegradable PVC properties while maintaining a minimum tensile strength as measured through the ratio of peak tension force by cross sectional area. PVC powder samples with number‐average molecular weights ranging from 20.0 to 90.0 kDa and varying exposure times of a 100 kGy electron beam from 80 to 115 ms are employed for the study. Outcomes from PVC irradiation reveal the optimal parameters for inducing biodegradability in samples: an e‐beam exposure duration of 95 ms applied to PVC powder with a sample molecular weight of 20.0 kDa. Analysis of the ratio of number average molecular weight and weight average molecular weight demonstrates that shorter irradiation durations result in lower molecular weights and molecular weight is directly proportional to Tg and crystallinity. Future work aims to scale up the procedure to industrial applications and investigate the treatment's applicability to a variety of thermoplastics.

A Novel Fault Diagnosis Method for Rolling Bearings Based on Multi-Domain Adaptation Neural Network

In recent years, data-driven approaches have shown promise in fault diagnosis. However, in practical industrial applications, challenges such as discrepancies between source domain and target domain data distributions, coupled with limited labeled fault data, have hindered the performance of traditional domain adaptation algorithms in bearing fault diagnosis. To address this issue, this study introduces a novel method based on MDANN (Multi Domain Adaptation Neural Network) for diagnosing rolling bearing faults using unlabeled data. Initially, the raw vibration signals are preprocessed using wavelet packet decomposition and reconstruction (WPT), which helps minimize signal redundancy while preserving critical features. Following this, the multi-kernel maximum mean discrepancy (MK-MMD) algorithm is employed to compute the differences in input feature values, and the MDANN network parameters are adjusted via backpropagation, allowing the network to extract domain-invariant features. To ensure the unlabeled target domain data can be effectively used in training, a pseudo-labeling strategy is applied, where the label with the highest probability is treated as the true label, thereby enabling the model to learn from the target domain data and improve the acquisition of reliable diagnostic knowledge. The method is validated using two publicly available datasets, CWRU and PU, with experimental results demonstrating that it outperforms conventional domain adaptation techniques in terms of diagnostic accuracy. This indicates the proposed approach is highly effective in capturing transferable features and addressing the distributional differences between datasets.

Designing the Next Generation of Polymers with Machine Learning and Physics-Based Models

Abstract The development of next-generation polymers necessitates optimizing several key properties simultaneously, a task that is expensive and infeasible using traditional trial-and-error experimental approaches. A promising alternative is employing a combination of machine learning and physics-based tools to rapidly screen the polymer design space and provide suggestions of new polymers that meet the critical properties required for industrial applications. In this study, we introduce a comprehensive workflow that utilizes machine learning and molecular modeling approaches to design new polymers with the focus on improving five polymer properties: (1) glass transition temperature, (2) dielectric constant, (3) refractive index, (4) stress optic coefficient, and (5) linear coefficient of thermal expansion. Using a small dataset (<200 unique polymers), we developed quantitative structure-property relationships (QSPR) models to accurately predict the experimental polymer properties for both homo- and co-polymer systems. We tested several ML algorithms and identified the best models for predicting these polymer properties, achieving test set R2 greater than 0.77 across all properties. We then explored new polymers by creating a library of over ~10,000 homopolymers using R-group enumeration tools and applied the trained QSPR models to rapidly predict the five polymer properties. The predictions of QSPR models were used to create a multi-parameter optimization score, which helped downselect the large polymer space to ~10 promising candidates. The properties of these selected polymer candidates were subsequently validated with classical molecular dynamics simulations and density functional theory, revealing a strong correlation with the QSPR model predictions. Finally, one of the top candidates was validated by experiments, which showed good agreement against QSPR and physics-based models. Our workflow underscores the power of combining data-driven and theoretical methods in the polymer design process given a small dataset size, offering a valuable resource for experimentalists looking to leverage computer-aided strategies in materials innovation.

Designing Artificial Laccase Catalysts by Introducing Substrate Oxidation Metals into Oxygen-Reducing Metal-Organic Frameworks: Cu-doped ZIF-67.

Laccase, a multi-copper oxidase, is limited by its optimal temperature range and isolation costs. To overcome these challenges, we synthesized copper-doped zeolitic imidazolate framework-67 (Cu-ZIF-67) with 16 mol% Cu as an artificial laccase catalyst. The introduced Cu site acts as the phenol oxidation site, and Co-based ZIF-67 is the four-electron oxygen reduction site. Laccase also employs this division of oxidation and reduction sites. Cu-doped ZIF-67 demonstrated significant catalytic activity, superior to natural laccase, especially at elevated temperatures, and maintained stability across multiple reaction cycles. These findings suggest that Cu-doped ZIF-67 is a robust, reusable alternative for industrial applications requiring high thermal stability and efficient catalysis.

Frozen Patterns in Viscoelastic Droplets Impacting on a Subcooled Surface.

The impact of droplets on a cold surface is ubiquitous in nature and various industrial applications, ranging from the icing of supercooled droplets on aircraft to the solidification of ink droplets in 3D printing. However, our understanding of the impact dynamics of droplets of complex fluids on cold surfaces is still very limited. Here, we experimentally study the spreading and frozen patterns of viscoelastic polymer droplets falling onto a subcooled substrate. We observe that the maximum spreading diameter of post-impact droplets decreases with increasing the subcooling temperature and the polymer concentration. Remarkably, all experimental data for spreading collapse into a universal curve, following the classic theory that accounts for inertial, capillary, and viscous forces. Unexpectedly, we find that, in contrast to the case of pure fluids, which exhibits three frozen modes, only two distinct modes, namely, freezing and hierarchical cracking, can be observed for polymer droplets. Finally, based on the undercooling temperature and polymer concentration, we construct a phase diagram for characterizing the morphologies of all frozen patterns. We expect that our findings may have implications in understanding the solidification of complex fluids on cold surfaces, for instance, in the fields of spray coating, inkjet printing, and additive manufacturing.

Highly conductive alcohol‐processable n‐type conducting polymer enabled by finely tuned electrostatic interactions for green organic electronics

Solution‐processable conducting polymers open up a new era in organic electronics, fundamentally altering the processing methods of electronic devices. P‐type conducting polymers, exemplified by aqueous solution‐processed poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS), have been successfully commercialized. However, the performance of electron‐transporting (n‐type) materials remains considerably poorer. One of the primary challenges lies in striking a balance between conductivity and solvent processability. At present, most n‐type conducting polymers necessitate toxic solvents for processing, which contradicts environmentally sustainable principles and impedes their potential for large‐scale industrial applications. Herein, we developed an alcohol‐processable high‐performance n‐type conducting polymer, poly(3,7‐dihydrobenzo[1,2‐b:4,5‐b’]difuran‐2,6‐dione): poly(2‐ethyl‐2‐oxazoline) (PBFDO:PEOx), which utilizes electrostatic interactions between PEOx and PBFDO to simultaneously achieve high conductivity and alcohol‐processability. The PBFDO:PEOx films exhibit remarkable electrical conductivity exceeding 1000 S cm−1 with outstanding stability even at temperatures up to 250 °C, establishing it as a prominent green solvent‐processed n‐type conducting polymer that rivals the most advanced p‐type counterparts. Various applications including organic thermoelectric, electrochemical transistor and electrochromic devices were showcased, highlighting the broad potential of PBFDO:PEOx in advancing green organic electronics.

Axes-aligned non-linear optimized PnP algorithm

Pose estimation is an important component of many real-world computer vision systems. Most existing pose estimation algorithms need a large number of point correspondences to accurately determine the pose of an object. Since the number of point correspondences depends on the object’s appearance, lighting and other external conditions, detecting many points may not be feasible. In many real-world applications, the movement of objects is limited, e.g. due to gravity. Hence, detecting objects with only three degrees of freedom is usually sufficient. This allows us to improve the accuracy of pose estimation by changing the underlying equations of the perspective-n-point problem to three variables instead of six. By using the simplified equations, our algorithm is more robust against detection errors with limited point correspondences. In this article, we study three scenarios where such constraints apply. The first one is about parking a vehicle on a specific spot. Here, a stationary camera is detecting the vehicle to assist the driver. The second scenario describes the perspective of a moving camera detecting objects in its environment. This scenario is common for driver assistance systems, autonomous cars or mobile robots. Third, we describe a camera observing objects from a birds-eye view, which occurs in industrial applications. In all three scenarios, observed objects can only move in the ground plane and rotate around the vertical axis. Hence, three degrees of freedom are sufficient to estimate the pose. Experiments with synthetic data and real-world photographs have shown that our algorithm outperforms state-of-the-art pose estimation algorithms. Depending on the scenario, our algorithm is able to achieve 50% better accuracy, while being equally fast.

Recent Progress on 3D Printing of Lightweight Metal Thin-Walled Structures.

Metal thin-walled structures are ubiquitous in various industrial applications and fabricating them through additive manufacturing (AM) enables intricate thin-wall geometries with no assembly required. However, additively manufactured metal thin walls suffer from increased heat accumulation and reduced structural stability, making it difficult to print geometrically accurate thin walls with minimal distortion and defects. Thin-walled structures also experience different thermal histories and solidification conditions compared to bulk structures, leading to drastic differences in the microstructure and mechanical properties. In this review article, the AM processing of metal thin-walled structures will be introduced. An in-depth discussion on the design strategies of additively manufactured metal thin walls will be presented regarding the restrictions imposed by the AM technology, the integration of thin walls in lightweight structures, and novel design ideation through topology optimization. The effects that the thin wall design has on the geometrical accuracy, microstructure, and mechanical properties will then be elucidated. The utilization of AM for fabricating metal thin walls across various industries will also be summarized. Finally, this review article will close on some perspectives and future work on the AM of metal thin-walled structures.

Surface defect inspection of industrial products with object detection deep networks: a systematic review

One of the focal points in industrial product defect detection lies in the utilization of deep learning-based object detection algorithms. With the continuous introduction of these algorithms and their refined models, notable achievements have been attained. However, challenges persist in industrial settings, such as substantial variations in defect scales, the delicate balance between accuracy and speed, and the detection of small objects. Various methods have been proposed to address these challenges and propel the advancement of defect detection. To comprehensively review the latest developments in deep learning-based industrial product defect detection algorithms and foster further progress, this paper encompasses typical datasets and evaluation metrics used in industrial product defect detection, traces the development history of supervised one-stage and two-stage object detection algorithm-based and unsupervised algorithm-based industrial defect detection methods, discusses major challenges, and outlines future directions. It highlights the potential for further improving the accuracy, speed, and reliability of defect detection systems in industrial applications.

A Universal Plug-and-Play Approach to In Situ Multinuclear Magnetic Resonance Analysis of Electrochemical Phenomena in Commercial Battery Cells.

Advancing electrochemical energy storage devices relies on versatile analytical tools capable of revealing the molecular mechanisms behind the function and degradation of battery materials in situ. The nuclear magnetic resonance phenomenon plays a pivotal role in fundamental studies of energy materials and devices because of its exceptional sensitivity to local environments and the dynamics of many electrochemically relevant elements. The jelly roll architecture is one of the most energy-dense and, therefore, most popular concepts implemented in pouch, prismatic, and cylindrical Li- and Na-ion cells. Such widely commercialized designs, however, represent a significant obstacle for a range of powerful in situ magnetic resonance-based methodologies due to negligible radio frequency electromagnetic field penetration through conductive metal casings and current collectors. In this work, we introduce an experimental setup that enables direct RF wave transmission through the cell terminals and current collectors, and provides efficient excitation and detection of NMR signals. An RF adapter designed as a plug-and-play device effectively turns the battery cell into an NMR probe that can be tuned to a broad range of Larmor frequencies. Due to its exceptional sensitivity, versatility, and multinuclear capability, the proposed methodology is suitable for fundamental research and industrial high-throughput screening applications. In situ NMR lineshapes provide a direct quantitative description of the chemical environments of electrochemically active elements and enable new metrics for the accurate assessment of state-of-charge (SoC) and state-of-health (SoH). Specifically, in commercial pouch cells based on lithium cobalt oxide, lithium nickel manganese cobalt oxide, and sodium nickel iron manganese oxide chemistries, 7Li and 23Na NMR data unambiguously show electrochemical transformations between intercalated and metallic forms of charge carrier ions, and reveal anisotropic magnetic properties of the electrode coating.

Effect of catalyst and oxidant concentrations in a TEMPO oxidation system on the production of cellulose nanofibers.

In traditional TEMPO oxidation systems, the high cost of TEMPO catalysts has been a significant barrier to the industrialization of oxidized CNF. From an economic perspective, presenting the characteristics of various CNFs produced with the oxidation systems with reduced catalyst usage could facilitate the industrial application of CNF across a wide range of fields. In this study, it was demonstrated that reducing the amount of TEMPO catalyst used (from 0.1 to 0.05 mmol g-1) in a conventional oxidation system increased the carboxylate content by approximately 6.3%. Furthermore, the activation of hydroxyl amine TEMPO, which is generated after the oxidation reaction of cellulose, was enhanced by adjusting the dosage of the inexpensive oxidant NaClO, leading to a 20% improvement in carboxylate content. This suggests that controlling the amount of NaClO as an oxidant can be a key parameter in adjusting the dosage of TEMPO to achieve the targeted degree of surface substitution. Results from the dispersion stability, UV-transmittance, and morphological properties of TEMPO-oxidized CNF using microfluidizing treatment showed that high carboxylate content plays a crucial role in producing high-purity CNF suspensions, which are small, uniform, and free from microfibers. Additionally, by varying the number of mechanical treatments applied to the oxidized cellulose, various types of CNF suspensions with different mean widths were obtained. We expect that these findings offer meaningful insights to end-users seeking a breakthrough in the performance limitations of final applications using cellulose nanomaterials.

Antimicrobial Properties and Therapeutic Potential of Bioactive Compounds in Nigella sativa: A Review.

Nigella sativa (N. sativa; Ranunculaceae), commonly referred to as black cumin, is one of the most widely used medicinal plants worldwide, with its seeds having numerous applications in the pharmaceutical and food industries. With the emergence of antibiotic resistance in pathogens as an important health challenge, the need for alternative microbe-inhibitory agents is on the rise, whereby black cumin has gained considerable attention from researchers for its strong antimicrobial characteristics owing to its high content in a wide range of bioactive compounds, including thymoquinone, nigellimine, nigellidine, quercetin, and O-cymene. Particularly, thymoquinone increases the levels of antioxidant enzymes that counter oxidative stress in the liver. Additionally, the essential oil in N. sativa seeds effectively inhibits intestinal parasites and shows moderate activity against some bacteria, including Bacillus subtilis and Staphylococcus aureus. Thymoquinone exhibits minimum inhibitory concentrations (MICs) of 8-16 μg/mL against methicillin-resistant Staphylococcus aureus (MRSA) and exhibits MIC 0.25 µg/mL against drug-resistant mycobacteria. Similarly, quercetin shows a MIC of 2 mg/mL against oral pathogens, such as Streptococcus mutans and Lactobacillus acidophilus. Furthermore, endophytic fungi isolated from N. sativa have demonstrated antibacterial activity. Therefore, N. sativa is a valuable medicinal plant with potential for medicinal and food-related applications. In-depth exploration of the corresponding therapeutic potential and scope of industrial application warrants further research.

LC-MS/MS Profiling, Biological Activities and Molecular Docking Studiesof Simmondsia Chinensis Leaves.

This study aimed to assess the biological activities of Tunisian Simmondsia chinensis and characterize their potential bioactive compounds. Different extracts of S. chinensis were tested for their antioxidant, antibacterial, anti α-amylase, and anti-acetylcholinesterase activities through in vitro assays. The methanolic extract exhibited the highest levels of antioxidant activity and total phenolic content (976.03 GAE/g extract) compared to the other extracts. Additionally, it demonstrated a substantial anti-acetylcholinesterase activity (PI= 75%) and potent antibacterial property, particularly against Enterococcus faecalis, Listeria monocytogenes, Bacillus cereus, Bacillus subtilis, and Salmonella enterica. The IC50 values of ethyl acetate and methanolic extracts against α-amylase were 42 μg/mL and 40 μg/mL, respectively, indicating potent anti-diabetic effects. HPLC-ESI-MS/MS analyses identified flavonoids and lignans as the major phenolic compounds in the methanolic extract. To better comprehend the mechanisms behind inhibitory effects on α-amylase and acetylcholinesterase enzymes, a molecular docking study was conducted. Consequently, these findings indicate that S. chinensis is a highly valuable natural resource with potential industrial applications.

Advancements in Desilylation Reactions for the Synthesis of Valuable Organic Molecules.

Silicon, due to its abundance, non-toxicity, and cost-effectiveness, is a critical element in the earth's crust with significant industrial applications. In organic chemistry, main group elements, and in particular silicon, are extensively utilized as versatile synthetic intermediates. Despite the current challenges associated with harsh reaction conditions and unsustainable practices in synthesizing crucial organic structural molecules, desilylation reactions have emerged as a facilitative method, offering milder conditions and operational simplicity. This review provides a comprehensive analysis of recent advancements in the synthesis of valuable organic molecules through two distinct desilylation reactions. It systematically presents the synthesis of a variety of derivatives, such as furan, alcohol, N-heterocyclic, and ketone, highlighting the broad substrate tolerance of these reactions. This broad functional group compatibility suggests a promising future for the synthesis of a wide range of bioactive molecules, underscoring the significant potential of desilylation in contemporary organic synthesis.

Highly conductive alcohol-processable n-type conducting polymer enabled by finely tuned electrostatic interactions for green organic electronics.

Solution-processable conducting polymers open up a new era in organic electronics, fundamentally altering the processing methods of electronic devices. P-type conducting polymers, exemplified by aqueous solution-processed poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS), have been successfully commercialized. However, the performance of electron-transporting (n-type) materials remains considerably poorer. One of the primary challenges lies in striking a balance between conductivity and solvent processability. At present, most n-type conducting polymers necessitate toxic solvents for processing, which contradicts environmentally sustainable principles and impedes their potential for large-scale industrial applications. Herein, we developed an alcohol-processable high-performance n-type conducting polymer, poly(3,7-dihydrobenzo[1,2-b:4,5-b']difuran-2,6-dione): poly(2-ethyl-2-oxazoline) (PBFDO:PEOx), which utilizes electrostatic interactions between PEOx and PBFDO to simultaneously achieve high conductivity and alcohol-processability. The PBFDO:PEOx films exhibit remarkable electrical conductivity exceeding 1000 S cm-1 with outstanding stability even at temperatures up to 250 °C, establishing it as a prominent green solvent-processed n-type conducting polymer that rivals the most advanced p-type counterparts. Various applications including organic thermoelectric, electrochemical transistor and electrochromic devices were showcased, highlighting the broad potential of PBFDO:PEOx in advancing green organic electronics.

PEDOT:PSS Nanoparticle Membranes for Organic Solvent Nanofiltration.

Recycling of valuable solutes and recovery of organic solvents via organic solvent nanofiltration (OSN) are important for sustainable development. However, the trade-off between solvent permeability and solute rejection hampers the application of OSN membranes. To address this issue, the poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) nanoparticle membrane with hierarchical pores is constructed for OSN via vacuum filtration. The small pores (the free volume of the polymer chain) charge for the solute rejection (high rejection efficiency for low molecule weight solute) and allow solvent passing while the large pores (the void between two PEDOT:PSS nanoparticles) promote the solvent transport. Owing to the lack of connectivity among the large pores, the fabricated PEDOT:PSS nanoparticle membrane enhanced solvent permeance while maintaining a high solute rejection efficiency. The optimized PEDOT:PSS membrane affords a MeOH permeance of 7.2 L m-2 h-1 bar-1 with over 90% rejection of organic dyes, food additives, and photocatalysts. Moreover, the rigidity of PEDOT endows the membrane with distinctive stability under high-pressure conditions. The membrane is used to recycle the valuable catalysts in a methanol solution for 150h, maintaining good separation performance. Considering its high separation performance and stability, the proposed PEDOT:PSS membrane has great potential for industrial applications.

Boosting the Quantum Efficiency of Ionic Carbon Nitrides in Photocatalytic H2O2 Evolution via Controllable n → π* Electronic Transition Activation.

Hydrogen peroxide (H2O2) is a crucial chemical used in numerous industrial applications, yet its manufacturing relies on the energy-demanding anthraquinone process. Solar-driven synthesis of H2O2 is gaining traction as a promising research area, providing a sustainable method for its production. Herein, a controllable activation of n → π* electronic transition is presented to boost the photocatalytic H2O2 evolution in ionic carbon nitrides. This enhancement is achieved through the simultaneous introduction of structural distortions and defect sites (─C ≡ N groups and N vacancies) into the KPHI framework. The optimal catalyst (2%Ox-KPHI) reached an apparent quantum yield of 41% at 410nm without the need for any cocatalysts, outperforming most previously reported carbon nitride-based photocatalysts. Extensive experimental characterizations and theoretical calculations confirm that a corrugated configuration and the presence of defects significantly broaden the light absorption profile, improve carrier separation and migration, promote O2 adsorption, and lower the energy barriers for H2O2 desorption. Transient absorption spectroscopy indicates that the enhanced photocatalytic performance of 2%Ox-KPHI is largely attributed to the preferential migration of electrons at defect sites over extended timescales, following the diffusion of geminate carriers across the PHI sheets.

Maltose gradient-induced biosensor-based high-throughput screening for directed evolution of maltogenic amylase from Bacillus stearothermophilus

Maltogenic amylase is a starch-hydrolyzing enzyme commonly used in bread baking and high-concentration maltose syrup production. However, low catalytic activity limits its industrial application. Improving catalytic activity based on molecular modification and directed evolution requires a High-Throughput Screening (HTS) method. In this study, a maltose gradient-induced (MaGI) biosensor was designed and applied for the directed evolution of maltogenic amylase AmyM, showing a good positive correlation between enzyme activity and fluorescence. The MaGI biosensor detected maltose and maltogenic amylase activity efficiently and specifically. Two mutants, Q440N and S442N/Q661L, were identified through the screening of 3000 mutants using the MaGI biosensor, showing a significant increase in catalytic activity of 35.56 % and 24.51 %, respectively, compared to the wild-type. Meanwhile, the t1/2 of Q440N and S442N/Q661L at 60 °C increased by 58.53 % and 66.66 %, respectively. In industrial applications, the enhancement of catalytic activity and stability is conducive to improving production efficiency and reducing costs. MD simulation has found that when modifying multidomain enzymes, distal mutations can enhance catalytic activity. In conclusion, the developed MaGI biosensor is a promising tool for high-throughput and specific detection of maltose.