Complex Equipment Research Articles

Screen-Assisted Self-Spreading of TiO2 Precursor Solution on FTO Substrates for High-Quality Electron Transport Layers in Perovskite Solar Cells.

The electron transport layer (ETL) plays a critical role in efficient and stable perovskite solar cells (PSCs). The current effective method for the large-scale preparation of metal oxide ETLs is mainly based on expensive sputtering processes. Here, a screen-assisted self-spreading method is proposed as a novel approach to prepare uniformly thin and conformal TiO2 films on a rough fluorine-doped tin oxide (FTO) substrate as an ETL in planar PSCs. The TiO2 ETL deposited by this method exhibited good coverage and homogeneity on the rough FTO substrate, thereby minimizing interfacial recombination. The photovoltaic performance of the PSCs fabricated by this method is superior to that of the cells fabricated by spin coating, especially in terms of the fill factor. The performance enhancement can be attributed to the complete coverage of the FTO substrate by the conformal TiO2 film, confirming the effectiveness and reliability of the proposed method for the preparation of the TiO2 ETL. The advantages of this method lie in its scalability to prepare oxide films with a large area, eliminating the requirement of complex equipment, such as spinners, sputters, or physical vapor deposition equipment.

GC×GC-TOFMS Analysis of Fecal Metabolome Stabilized Using an At-Home Stool Collection Device

Stool is a mixture of excrement, microbiota, enzymes, undigested material, and small molecules. Fecal metabolomics has gained interest recently, owing to advances in metabolomics and growing research into both the host’s physiology and the gut microbiome. One challenge with fecal metabolomics is preserving the sample integrity from collection until analysis, as the microbiota and enzymes continue to alter the metabolome following defecation. Currently, flash-freezing or lyophilization are utilized to minimize post-collection metabolome changes; however, this requires complex equipment and immediate processing, precluding the possibility for at-home sampling. Commercial devices containing stabilizing solvents have been developed to facilitate at-home collection, ambient transport, and sample storage. Here, we explore the efficacy of a commercially available stool collection device with a stabilization reagent tailored to fecal metabolomics. Stool samples from six donors were either processed shortly post-collection or stored at room temperature for seven days in the tube, with and without the stabilization reagent. Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS)-based untargeted metabolomics was utilized for analyzing extracted metabolites. Chemometric analysis was used to evaluate the performance of the device. We found that the device with the stabilization reagent minimized changes in the metabolite profile relative to unstabilized stool left at room temperature for one week.

Electrochemical Quantification of Tobramycin Retention in Pseudomonas Aeruginosa as Antimicrobial Susceptibility Indicator

The emergence and spread of bacterial resistance to antibiotics has developed into one of the most challenging threats to public health. Antibiotic susceptibility tests (ASTs) for bacterial infections are now essential, because they provide guidance for physicians in the selection of antibiotics, to which bacteria will respond. Most current AST methods require long periods of time, because of bacterial growth and incubation, leading to a prolonged and overuse of broad-spectrum antibiotics. Thus, there is a growing demand for methods and technologies that enable rapid antibiotic susceptibility assessment. Due to advantages related to cost-effectiveness, rapid response time and high sensitivity,electrochemical detection methods are promising analytical tools that can successfully quantify antibiotic uptake and retention in clinically relevant bacterial strains. This study presents the electroanalytical quantification of tobramycin (TOB) retention in susceptible and resistant bacterial strains of Pseudomonas aeruginosa. The electrochemical behavior of TOB was characterized by voltammetry, identifying redox potentials, the current dependence on pH conditions, and the detection limit at unmodified glassy carbon electrodes. The presented methodology was able to distinguish between susceptible and resistant bacterial strains, and is also capable of identifying varying degrees of resistance against TOB. The presented approach detects the immediate interaction of bacteria with an antibiotic, without the need of complex and cost-intense equipment related to genomic testing methods

Research on standardization of power transformer monitoring and early warning based on multi-source data

To meet the growing demand for integrated monitoring of complex power grid equipment, it is necessary to improve the situational awareness model of power transformers. The model is expected to assist monitoring personnel in timely identifying transformers with deteriorating trends among massive and discrete monitoring information, and to make responses in advance. However, the current transformer state awareness technology generally has the problem of single data source and poor timeliness, and still requires monitoring personnel to make artificial analysis and prediction in combination with telemetry information, which cannot fully meet the requirements of power grid equipment monitoring. This paper is based on multi-source data fusion technology, through associating and mining transformer alarm information, equipment maintenance records and power transmission and transformation online monitoring data, to extract the dimension features of transformer operation situation assessment. By constructing a multi-layer perceptron model, a transformer state transition model based on the principle of Markov chain is established, which can predict possible defects 2 h in advance and achieve good results, and determine the transformer state early warning index, providing sufficient time for monitoring personnel to deploy transformer operation and maintenance work in advance. Finally, the effectiveness of the method proposed in this paper is proved by the case of transformer crisis state in a city substation, and the method proposed in this paper has important significance for transformer state early warning.

A resource scheduling model for complex equipment development projects considering random overlapping and random feedback of activities

ABSTRACT Aiming to address the time and resource conflict problem caused by multiple tasks and frequent overlapping at each level in the process of complex equipment development, this article constructs a design structure matrix–graphical evaluation and review technique (DSM-GERT) hybrid model, takes the shortest duration of the project as the optimization objective and solves it using the genetic algorithm. Under the conditions of meeting the constraints of time, cost and quality, the project’s parameters are improved by adjusting the resource inputs. The study found that when project resources are insufficient, intertask messaging should be avoided as much as possible. If messaging is initiated, early messaging can be effective in reducing resource consumption, and decision makers can make trade-offs based on their preferences for resources.

Membranes Based on Aminated Graphene Oxide with High Selectivity Toward Organic Substances.

In this work, membranes based on graphene oxide, modified with oleylamine, have been prepared by a simple wet chemistry protocol without the use of complex equipment, elevated temperature, and additional reagents. The membrane material was characterized by a set of physicochemical methods: thermogravimetric analysis, Fourier transform infrared spectroscopy, X-ray diffractometry, and X-ray photoelectron spectroscopy. The prepared membranes are stable in both aqueous and organic media. The membranes have a high flux for organic substances and do not permeate water at room temperature and atmospheric pressure. The selectivity of the membranes toward organic substances increases with their thickness. The highest flux among the tested organic liquids is registered for methanol. The membranes have high selectivity toward ethanol/1-butanol and acetone/1-butanol pairs, which opens up the possibility of separating actual industrial mixtures. The membrane retains 90% of methylene blue from the alcohol solution. Our work expands the possibilities of using modified GO-based membranes in purification and filtration technologies.

Colorimetric IPN hydrogels embedded with colloidal photonic crystals: A novel approach for the detection of ethanol and Ba2+ ions in water

A critical bottleneck in sensor technology is the rapid and precise detection of specific analytes in complex matrices, hindering advancements in environmental monitoring, healthcare, and industrial process control. This study addresses this challenge by introducing a novel composite hydrogel sensor designed for rapid and selective detection of ethanol and barium ions (Ba2+) in aqueous environments. The sensor integrates interpenetrating network (IPN) hydrogels with embedded colloidal photonic crystals (CPCs), synthesized via a solution-based polymerization approach. This innovative configuration allows CPCs to dynamically adjust their photonic bandgap in response to environmental changes, manifesting as a visible, colorimetric shift. This response stems from the synergy between the mechanical properties of the IPN hydrogel and the optical sensitivity of CPCs. Upon exposure to analytes such as ethanol and Ba2+, the sensor exhibits a rapid and reversible color transition that is directly proportional to their concentration. Notably, ethanol (0 vol%–80 vol%) and Ba2+ (5–17.5 mM) induce a distinct blueshift in the photonic bandgap and trigger a color change from red–orange to green due to the alteration in the swelling behavior of the IPN hydrogel, affecting its lattice constant. The IPN hydrogel–CPC composite demonstrates exceptional operational stability and facilitates rapid detection, making it ideal for on-site applications without the need for complex equipment. These characteristics make the composite hydrogel sensor a promising candidate for environmental monitoring, industrial process control, and public health diagnostics, paving the way for the development of next-generation responsive sensor materials.

Efficient low-grade heat harvesting with flexible paper-based thermoelectric generators fabricated by facile process

The study presents the fabrication and characterization of paper-based thermoelectric generators (PTGs) using p-type Bi0.5Sb1.5Te3 (BST) and n-type Bi2Te2.7Se0.3 (BTS) thermoelectric (TE) materials, deposited via a novel cotton swab powder deposition technique. This method eliminates the need for complex equipment or high-temperature processes, reducing fabrication complexity and enabling large-scale production. The key advancements of this method are its simplicity, cost-effectiveness, and practical applicability, ensuring uniform coating and good material adherence to the paper substrate. The PTGs, consisting of five p-n pairs interconnected with various materials, were laminated for enhanced stability and humidity protection. Experimental measurements under temperature gradients(ΔTs) of 5–30 °C revealed an optimal configuration achieving an open-circuit voltage(Voc) of 42.60 mV, internal resistance(Rint) of 42.50kΩ, short-circuit current(Isc) of 1.00µA, and maximum power output(Pmax) of 10.65nW for the carbon paste/copper sheet interconnected PTG at ΔT of 30 °C. The obtained normalized power density is 5.68nWcm-2K-1pair-1, which is one of the best among the reported Bi2Te3-based PTGs. A preliminary demonstration on an arm showed the PTG generating an 11.95 mV output voltage in hot weather conditions (ΔT ≈ 8 °C), effectively harnessing body heat for wearable electronics. This work highlights a simple, cost-effective, and scalable method for fabricating PTGs for sustainable energy harvesting in low-power wearable applications. Future efforts will focus on optimizing materials, fabrication techniques, and novel designs to enhance efficiency and scalability.

An improved dynamical variational autoencoder framework for predicting aero-engine remaining useful life

Abstract Accurate remaining useful life (RUL) prediction is vital for ensuring equipment safety, optimizing maintenance schedules, and reducing operational costs. Deep learning networks have emerged as effective means for RUL prediction. However, these methods encounter challenges of multiple sensors and data noise when handling large and complex industrial equipment, which impede the prediction model’s ability to generalize. To address these issues, this paper proposes an improved Dynamical Variational Autoencoders (DVAE) Framework combining principal component analysis (PCA) and long short-term memory (LSTM) networks. The effectiveness of our DVAE RUL prediction framework is validated through experiments conducted on the NASA C-MAPSS dataset. Results show that the approach significantly improves the prediction accuracy.

Aviation armament system-of-systems modeling and identification method of vulnerable nodes based on interdependent network

Aiming at the problem that it is difficult to build model and identify the vulnerable equipment for aviation armament System-of-Systems (SoS) due to complex equipment interaction relationships and high confrontation, the interdependent network theory is introduced to solve it. Firstly, a two-layer heterogeneous interdependent network model for aviation armament SoS is proposed, which reflects the information interaction, functional dependency and inter-network dependence effectively. Secondly, using the attack cost to describe the confrontation process and taking the comprehensive impact on kill chains as the entry point, the node importance index and the attack cost measurement method are constructed. Thirdly, the identification of vulnerable nodes is transformed into the optimization problem of node combinatorial selection, and the vulnerable node identification method based on tabu search is proposed. Based on vulnerable nodes, a robustness enhancement strategy for aviation armament SoS network is presented. Finally, the above methods are used to an aerial confrontation SoS, and the results verify the rationality and effectiveness of the proposed methods.

Detecting microfragments of glass in scar tissues

The identification of traumatic causes in the case of damage caused by sharp objects has always been one of the main issues of interest to the investigating authorities. Currently, forensic medicine allows for determining a specific acute traumatic object, including glass fragments. However, in the available literature, no information is available about the possibility of detecting microfragments of glass in the scar. Herein, the case of a man who suffered a chest wound is presented. The patient was brought to the hospital, where the wound was sutured. In the medical records, the wound was described as a stab wound and could have been caused by a knife. The accused categorically denied inflicting a knife wound on this person and argued that the victim was in a state of severe alcoholic intoxication and fell repeatedly, including on a sideboard, glass from which was found during an inspection of the scene. Four months later, the victim died from alcohol poisoning. A forensic medical examination was conducted to confirm the possibility of injury from glass fragments from the sideboard. A scar was identified on the victim’s chest. Microparticles were found in the scar tissue, and their characteristics led to the conclusion that they were microfragments of colorless, transparent glass. Determining the presence of glass microfragments in scar tissue does not require complex technical equipment and are common in wide expert practice; their use has confirmed the possibility of detecting glass microfragments not only in soft tissues along the wound channel but also in scar tissue after wound healing.

Separation methods for food protein purification and analysis

The extraction, separation, and purification of dietary proteins from a variety of food sources are crucial for their targeted use in food applications. To achieve this, proteins should be effectively separated from non-protein components such as cell wall structures, polysaccharides, and lipids. Traditional protein purification methods can be time-consuming, highlighting the need for automated, cost-effective, and sustainable alternatives. This comprehensive review critically assesses various protein purification instruments from an analytical perspective, weighing their advantages and disadvantages. The methods under evaluation include ultrafiltration, sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), fast protein liquid chromatography (FPLC), high-performance liquid chromatography (HPLC), ultra performance liquid chromatography (UPLC), and microfluidic chips. Among these, FPLC stands out as an affordable and efficient technique that allows for high protein recovery. However, HPLC and UPLC provide faster results but may denature proteins, leading to lower recovery rates. Ultrafiltration is a cost-effective and straightforward method that doesn’t require complex equipment. Microchip-based approaches are emerging as innovative techniques for rapidly analyzing small samples. While SDS-PAGE is user-friendly, it denatures proteins, particularly those linked to other biomolecules. The choice of the most appropriate instrument depends on factors such as cost, energy efficiency, processing time, the characteristics of the target protein, desired outcomes, protein recovery, and resource availability. By critically examining these analytical instruments for protein purification, this review aims to assist researchers and practitioners in selecting the most suitable method for their specific needs, ultimately promoting efficient and successful protein purification endeavors in the field of food science and technology.

An Alternative Photothrombotic Model of Transient Ischemic Attack.

Animal models mimicking human transient ischemic attack (TIA) and cerebral microinfarcts are essential tools for studying their pathogenetic mechanisms and finding methods of their treatment. Despite its advantages, the model of single arteriole photothrombosis requires complex experimental equipment and highly invasive surgery, which may affect the results of further studies. Hence, to achieve high translational potential, we focused on developing a TIA model based on photothrombosis of arterioles to combine good reproducibility and low invasiveness. For the first time, noninvasive laser speckle contrast imaging (LSCI) was used to monitor blood flow in cerebral arterioles and reperfusion was achieved. We demonstrate that irradiation of mouse cerebral cortical arterioles using a 532-nm laser with a 1-mm-wide beam at 2.4 or 3.7 mW for 55 or 40 s, respectively, after 15 mg/kg intravenous Rose Bengal administration, induces similar ischemia-reperfusion lesions resulting in microinfarct formation. The model can be used to study the pathogenesis of spontaneously developing cerebral microinfarcts in neurodegeneration. Reducing the exposure times by 10 s while maintaining the same other parameters caused photothrombosis of the arteriole with reperfusion in less than 1 h. This mode of photodynamic exposure caused cellular and subcellular level ischemic changes in neurons and promoted the activation of astrocytes and microglia in the first day after irradiation, but not later, without the formation of microinfarcts. This mode of photodynamic exposure most accurately reproduced human TIA, characterized by the absence of microinfarcts.

Fabrication of flexible Au microsensors by a simple stamping method for the point-of-care direct detection of trace leucomalachite green

Leucomalachite green (LMG) is the metabolic product of malachite green, which is a common aquaculture antimicrobial known for its high toxicity and long persistence. To date, few reports on the direct detection of LMG using electrochemical point-of-care (POC) sensors have been published. In the present work, flexible gold (Au) microsensors were fabricated from nanometer-thin Au leaves for the first time on different substrates, such as polyethylene terephthalate, thermoplastic polyurethane, silicone, polyvinyl chloride wristbands, and nitrile gloves, through simple hot stamping. The entire fabrication process did not involve toxic solvents and complex equipment, and the material cost was only $0.1 per microsensor. The morphology, electrochemical properties, and sensing performance of the microsensors were characterized by scanning electron microscopy, cyclic voltammetry, and differential pulse voltammetry. Benefiting from the excellent catalytic activity and conductivity of Au, the microsensors exhibited outstanding direct sensing capability for LMG with a detection limit of 6.3 nM. Moreover, the microsensors demonstrated excellent recovery rates in real sample analysis, validating their practicality. The technology reported in this work provides a pioneering and effective solution for the sensitive and convenient POC direct detection of LMG and further allows for the fabrication of Au microsensors on different flexible and conformal substrates, offering sensitive solutions for POC detection in environmental, food safety, and healthcare applications.

A Single-Camera-Based Three-Dimensional Velocity Field Measurement Method for Granular Media in Mass Finishing with Deep Learning.

Surface treatment processes such as mass finishing play a crucial role in enhancing the quality of machined parts across industries. However, accurate measurement of the velocity field of granular media in mass finishing presents significant challenges. Existing measurement methods suffer from issues such as complex and expensive equipment, limited to single-point measurements, interference with the flow field, and lack of universality in different scenarios. This study addresses these issues by proposing a single-camera-based method with deep learning to measure the three-dimensional velocity field of granular flow. We constructed a complete measurement system and analyzed the accuracy and performance of the proposed method by comparing the measurement results with those of the traditional DIC algorithm. The results show that the proposed method is very accurate in measuring spatial displacement, with an average error of less than 0.07 mm and a calculation speed that is 1291.67% of the traditional DIC algorithm under the same conditions. Additionally, experiments in a bowl-type vibratory finishing machine demonstrate the feasibility of the proposed method in capturing the three-dimensional flow of granular media. This research not only proposed a novel method for three-dimensional reconstruction and velocity field measurement using a single-color camera, but also demonstrated a way to combine deep learning with traditional optical techniques. It is of great significance to introduce deep learning to improve traditional optical techniques and apply them to practical engineering measurements.

Embedded-deep-learning-based sample-to-answer device for on-site malaria diagnosis.

Improvements in digital microscopy are critical for the development of a malaria diagnosis method that is accurate at the cellular level and exhibits satisfactory clinical performance. Digital microscopy can be enhanced by improving deep learning algorithms and achieving consistent staining results. In this study, a novel miLab™ device incorporating the solid hydrogel staining method was proposed for consistent blood film preparation, eliminating the use of complex equipment and liquid reagent maintenance. The miLab™ ensures consistent, high-quality, and reproducible blood films across various hematocrits by leveraging deformable staining patches. Embedded-deep-learning-enabled miLab™ was utilized to detect and classify malarial parasites from autofocused images of stained blood cells using an internal optical system. The results of this method were consistent with manual microscopy images. This method not only minimizes human error but also facilitates remote assistance and review by experts through digital image transmission. This method can set a new paradigm for on-site malaria diagnosis. The miLab™ algorithm for malaria detection achieved a total accuracy of 98.86% for infected red blood cell (RBC) classification. Clinical validation performed in Malawi demonstrated an overall percent agreement of 92.21%. Based on these results, miLab™ can become a reliable and efficient tool for decentralized malaria diagnosis.

A Novel Intelligent Condition Monitoring Framework of Essential Service Water Pumps

Essential service water pumps are necessary safety devices responsible for discharging waste heat from containments through seawater; their condition monitoring is critical for the safe and stable operation of seaside nuclear power plants. However, it is difficult to directly apply existing intelligent methods to these pumps. Therefore, an intelligent condition monitoring framework is designed, including the parallel implementation of unsupervised anomaly detection and fault diagnosis. A model preselection algorithm based on the highest validation accuracy is proposed for anomaly detection and fault diagnosis model selection among existing models. A novel information integration algorithm is proposed to fuse the output of anomaly detection and fault diagnosis. According to the experimental results of modules, a kernel principal component analysis using mean fusion processing multi-channel data (AKPCA (fusion)) is selected, and a support vector machine using mean fusion processing multi-channel data (SVM (fusion)) is selected. The overall test accuracy and false negative rate of AKPCA (fusion) are 0.83 and 0.144, respectively, and the overall test accuracy and f1-score of SVM (fusion) are 0.966 and 1, respectively. The test results of AKPCA (fusion), SVM (fusion), and the proposed information integration algorithm show that the information integration algorithm successfully avoids a lack of abnormal status information and misdiagnosis. The proposed framework is a meaningful attempt to achieve the intelligent condition monitoring of complex equipment.

Enhanced Point-of-Care SARS-CoV-2 Detection: Integrating RT-LAMP with Microscanning.

The COVID-19 pandemic has highlighted the urgent need for rapid and accurate diagnostic methods for various infectious diseases, including SARS-CoV-2. Traditional RT-PCR methods, while highly sensitive and specific, require complex equipment and skilled personnel. In response, we developed an integrated RT-LAMP-MS assay, which combines rapid reverse transcription loop-mediated isothermal amplification (RT-LAMP) with microscanning (MS) technology for detecting SARS-CoV-2. The assay uses magnesium pyrophosphate formed during LAMP amplification as a visual marker, allowing direct observation via microscopy without the need for additional chemical indicators or probes. For the SARS-CoV-2/IC RT-LAMP-MS assay, the sample-LAMP reagent mixture was added to a microchip with SARS-CoV-2 primers and internal controls, then incubated at 62 °C for 30 min in a heat block, followed by amplification analysis using a microscanner. In clinical tests, the RT-LAMP-MS assay showed 99% sensitivity and 100% specificity, which is identical to the RT-LAMP results and comparable to the commercial AllplexTM SARS-CoV-2 assay results. Additionally, the limit of detection (LOD) was determined to be 10-1 PFU mL-1 (dynamic range: 103~10-1 PFU mL-1). The assay delivers results in 30 min, uses low-cost equipment, and demonstrates 100% reproducibility in repeated tests, making it suitable for point-of-care use in resource-limited settings.

Recent Advances in Biosensor Technology for Early-Stage Detection of Hepatocellular Carcinoma-Specific Biomarkers: An Overview.

Hepatocellular carcinoma is currently the most common malignancy of the liver. It typically occurs due to a series of oncogenic mutations that lead to aberrant cell replication. Most commonly, hepatocellular carcinoma (HCC) occurs as a result of pre-occurring liver diseases, such as hepatitis and cirrhosis. Given its aggressive nature and poor prognosis, the early screening and diagnosis of HCC are crucial. However, due to its plethora of underlying risk factors and pathophysiologies, patient presentation often varies in the early stages, with many patients presenting with few, if any, specific symptoms in the early stages. Conventionally, screening and diagnosis are performed through radiological examination, with diagnosis confirmed by biopsy. Imaging modalities tend to be limited by their requirement of large, expensive equipment; time-consuming operation; and a lack of accurate diagnosis, whereas a biopsy's invasive nature makes it unappealing for repetitive use. Recently, biosensors have gained attention for their potential to detect numerous conditions rapidly, cheaply, accurately, and without complex equipment and training. Through their sensing platforms, they aim to detect various biomarkers, such as nucleic acids, proteins, and even whole cells extracted by a liquid biopsy. Numerous biosensors have been developed that may detect HCC in its early stages. We discuss the recent updates in biosensing technology, highlighting its competitive potential compared to conventional methodology and its prospects as a tool for screening and diagnosis.

Origami-inspired microfluidic paper-based analytical device (μPAD) for microorganism detection

AbstractPathogenic microorganisms impose great risk especially in resource-limited settings due to inaccessibility of diagnostic tools and monitoring devices. This is mainly caused by current methods often being economically demanding and complex in practice; while these methods are sensitive and accurate, they rarely follow Point-of-care (POC) approaches, which is essential for rapid detection and intervention. Incorporating origami into paper-based analytical devices (μPAD) presents an innovative alternative, offering affordability, portability, and ease of disposal. Herein, a colorimetric origami μPAD that is suitable for use in POC applications was developed. The μPAD was fabricated via laser ablation utilizing PVDF and cellulose membranes. In order to develop the biosensor platform, fabrication parameters were optimized and hydrophilicity of PVDF membranes was improved using various solvents. The PVDF membranes were characterized through light microscopy imaging, protein adsorption assay and contact angle measurements. Then, optimization of the assay parameters was carried out in order to improve sensitivity and resolution of the μPAD, utilizing Box-Behnken experimental design. The responses generated by the origami μPAD in form of visible color development were then analyzed using image processing. After optimization is concluded, E. coli detection was carried out as a model system. Resulting calculations showed a limit of detection (LoD) of 2 CFU/mL and a dynamic working range up to 106 CFU/mL for E. coli. Overall, developed origami μPAD promises an economic advantage compared to conventional methods, and provides rapid and sensitive results without the requirement of expertise or complex equipment.