Plasma Surface Treatment of Cu Current Collectors for Improving the Electrochemical Performance of Si Anodes.

The practical utilization of Si electrodes is hindered by their substantial volume expansion during alloying and dealloying processes, which causes mechanical damage and separation from Cu current collectors. To alleviate the problem of Si composite detachment from Cu current collectors, the surface of the Cu current collectors is modified using atmospheric oxygen plasma. Plasma treatment improves the wetting ability of the Cu current collectors and, consequently, the coating quality of the Si electrodes. The uniform distribution of the Si electrode components reduces the sheet resistance and improves the adhesion properties of the Si electrodes containing surface-modified Cu current collectors. As a result, the volume expansion of Si during alloying and dealloying is reduced; this results in an excellent rate capability of 1584 mA h g-1 at a current density of 3.6 A g-1 (135% that of bare Cu) and excellent cycle performance of 1545 mA h g-1 after 300 cycles (Si electrodes with bare Cu exhibit 930 mA h g-1). Therefore, the developed plasma treatment method for Cu current collectors is expected to be an economical and efficient approach for improving the Li-ion battery performance.

Raspberry Pi Platform Wireless Sensor Node for Low-Frequency Impedance Responses of PZT Interface.

A wireless impedance monitoring system, called SSeL-Pi, is designed to have cheap, mobile, and handy practical features as compared to wired commercial impedance analyzers. A Raspberry Pi platform impedance sensor node is designed to measure signals at a low-frequency range of up to 100 kHz. The low-frequency impedance measurement via the proposed node has been combined with a new PZT interface technique for measuring local responses sensitive to structural damage. The new PZT interface can work as a surface-mounted or embedded sensor, and its local dynamic characteristics are numerically analyzed to pre-determine an effective impedance resonant frequency range of less than 100 kHz. Next, a software scheme was designed to visualize the input/output parameters of the proposed SSeL-Pi system (i.e., Raspberry Pi platform and PZT interface) and automate signal acquisition procedures of the impedance sensor node. The calibration for impedance signals obtained from the proposed system was performed by a series of procedures, from acquiring real and imaginary impedance to adjusting them with respect to a commercial impedance analyzer (HIOKI-3532). The feasibility of the wireless impedance monitoring system was experimentally evaluated for PZT interfaces that were subjected to various compressive loadings. The consistent results analyzed from signals measured by the SSeL-Pi and HIOKI 3532 systems were observed. Additionally, the strong relationships between impedance features (frequency shift and RMSD index) and compressive stresses of the PZT interfaces showed the potential for axial force/stress variation monitoring in real structures using the Raspberry Pi platform impedance sensor node and developed PZT interface.

Inflammation promotes synucleinopathy propagation.

The clinical progression of neurodegenerative diseases correlates with the spread of proteinopathy in the brain. The current understanding of the mechanism of proteinopathy spread is far from complete. Here, we propose that inflammation is fundamental to proteinopathy spread. A sequence variant of α-synuclein (V40G) was much less capable of fibril formation than wild-type α-synuclein (WT-syn) and, when mixed with WT-syn, interfered with its fibrillation. However, when V40G was injected intracerebrally into mice, it induced aggregate spreading even more effectively than WT-syn. Aggregate spreading was preceded by sustained microgliosis and inflammatory responses, which were more robust with V40G than with WT-syn. Oral administration of an anti-inflammatory agent suppressed aggregate spreading, inflammation, and behavioral deficits in mice. Furthermore, exposure of cells to inflammatory cytokines increased the cell-to-cell propagation of α-synuclein. These results suggest that the inflammatory microenvironment is the major driver of the spread of synucleinopathy in the brain.

Dendrite Suppression by Lithium-Ion Redistribution and Lithium Wetting of Lithium Zeolite Li2(Al2Si4O12) in Liquid Electrolytes.

Lithium metal is considered a next-generation anode material for high-voltage, high-energy-density batteries; however, its commercialization is limited because of dendrite formation during charging, which leads to short-circuiting and fire. Li metal is coated with a lithium zeolite Li2(Al2Si4O12) (bikitaite - BKT) for dendrite suppression. The BKT-coated Li metal anode exhibits enhanced cycle performance for both Li/LMO (over 982 cycles) and Li/Li cells (over 2000 h at 0.52.0 mAh cm-2 and 693 h at 2.0 mAh cm-2). Moreover, the voltage profile of the Li/Li cells deviates from the conventional Li plating behavior. We hypothesize that this is due to the Li wetting of the BKT particles during plating, which leads to the formation of an interconnected three-dimensional (3D) Li network. Furthermore, BKT, a Li conductor, promotes even Li+-ion distribution during plating, resulting in the uniform deposition of Li and, consequently, suppressed dendrite formation. This work provides evidence that BKT can be potentially used in Li metal batteries.

TNF-α promotes α-synuclein propagation through stimulation of senescence-associated lysosomal exocytosis.

Cell-to-cell propagation of α-synuclein is thought to be the underlying mechanism of Parkinson's disease progression. Recent evidence suggests that inflammation plays an important role in the propagation of protein aggregates. However, the mechanism by which inflammation regulates the propagation of aggregates remains unknown. Here, using in vitro cultures, we found that soluble factors secreted from activated microglia promote cell-to-cell propagation of α-synuclein and further showed that among these soluble factors, TNF-α had the most robust stimulatory activity. Treatment of neurons with TNF-α triggered cellular senescence, as shown by transcriptomic analyses demonstrating induction of senescence-associated genes and immunoanalysis of senescence phenotype marker proteins. Interestingly, secretion of α-synuclein was increased in senescent neurons, reflecting acquisition of a senescence-associated secretory phenotype (SASP). Using vacuolin-1, an inhibitor of lysosomal exocytosis, and RNAi against rab27a, we demonstrated that the SASP was mediated by lysosomal exocytosis. Correlative light and electron microscopy and immunoelectron microscopy confirmed that propagating α-synuclein aggregates were present in electron-dense lysosome-like compartments. TNF-α promoted the SASP through stimulation of lysosomal exocytosis, thereby increasing the secretion of α-synuclein. Collectively, these results suggest that TNF-α is the major inflammatory factor that drives cell-to-cell propagation of α-synuclein by promoting the SASP and subsequent secretion of α-synuclein.

Corroded Bolt Identification Using Mask Region-Based Deep Learning Trained on Synthesized Data.

The performance of a neural network depends on the availability of datasets, and most deep learning techniques lack accuracy and generalization when they are trained using limited datasets. Using synthesized training data is one of the effective ways to overcome the above limitation. Besides, the previous corroded bolt detection method has focused on classifying only two classes, clean and fully rusted bolts, and its performance for detecting partially rusted bolts is still questionable. This study presents a deep learning method to identify corroded bolts in steel structures using a mask region-based convolutional neural network (Mask-RCNN) trained on synthesized data. The Resnet50 integrated with a feature pyramid network is used as the backbone for feature extraction in the Mask-RCNN-based corroded bolt detector. A four-step data synthesis procedure is proposed to autonomously generate the training datasets of corroded bolts with different severities. Afterwards, the proposed detector is trained by the synthesized datasets, and its robustness is demonstrated by detecting corroded bolts in a lab-scale steel structure under varying capturing distances and perspectives. The results show that the proposed method has detected corroded bolts well and identified their corrosion levels with the most desired overall accuracy rate = 96.3% for a 1.0 m capturing distance and 97.5% for a 15° perspective angle.

Capsule-Like Smart Aggregate with Pre-Determined Frequency Range for Impedance-Based Stress Monitoring.

In this article, a new capsule-like smart aggregate (CSA) is developed and verified for impedance-based stress monitoring in a pre-determined frequency range of less than 100 kHz. The pros and cons of the existing smart aggregate models are discussed to define the requirement for the improved CSA model. The conceptual design and the impedance measurement model of the capsule-like smart aggregate (CSA) are demonstrated for concrete damage monitoring. In the model, the interaction between the CSA and the monitored structure is considered as the 2-degrees of freedom (2-DOF) impedance system. The mechanical and impedance responses of the CSA are described for two conditions: during concrete strength development and under compressive loadings. Next, the prototype of the CSA is designed for impedance-based monitoring in concrete structures. The local dynamic properties of the CSA are numerically simulated to pre-determine the sensitive frequency bands of the impedance signals. Numerical and experimental impedance analyses are performed to investigate the sensitivity of the CSA under compressive loadings. The changes in the impedance signals of the CSA induced by the compressive loadings are analyzed to assess the effect of loading directions on the performance of the CSA. Correlations between statistical impedance features and compressive stresses are also made to examine the feasibility of the CSA for stress quantification.

Smart PZT-Embedded Sensors for Impedance Monitoring in Prestressed Concrete Anchorage.

This study investigates the feasibility evaluation of smart PZT-embedded sensors for impedance-based damage monitoring in prestressed concrete (PSC) anchorages. Firstly, the concept of impedance-based damage monitoring for the concrete anchorage is concisely introduced. Secondly, a prototype design of PZT-embedded rebar and aggregate (so-called smart rebar-aggregate) is chosen to sensitively acquire impedance responses-induced local structural damage in anchorage members. Thirdly, an axially loaded concrete cylinder embedded with the smart rebar-aggregate is numerically and experimentally analyzed to investigate their performances of impedance monitoring. Additionally, empirical equations are formulated to represent the relationships between measured impedance signatures and applied compressive stresses. Lastly, an experimental test on a full-scale concrete anchorage embedded with smart rebar-aggregates at various locations is performed to evaluate the feasibility of the proposed method. For a sequence of loading cases, the variation in impedance responses is quantified to evaluate the accuracy of smart rebar-aggregate sensors. The empirical equations formulated based on the axially loaded concrete cylinder are implemented to predict compressive stresses at sensor locations in the PSC anchorage.

Optimal Localization of Smart Aggregate Sensor for Concrete Damage Monitoring in PSC Anchorage Zone.

This study investigates the feasibility of smart aggregate (SA) sensors and their optimal locations for impedance-based damage monitoring in prestressed concrete (PSC) anchorage zones. Firstly, numerical stress analyses are performed on the PSC anchorage zone to determine the location of potential damage that is induced by prestressing forces. Secondly, a simplified impedance model is briefly described for the SA sensor in the anchorage. Thirdly, numerical impedance analyses are performed to explore the sensitivities of a few SA sensors in the anchorage zone under the variation of prestressing forces and under the occurrence of artificial damage events. Finally, a real-scale PSC anchorage zone is experimentally examined to evaluate the optimal localization of the SA sensor for concrete damage detection. Impedance responses measured under a series of prestressing forces are statistically quantified to estimate the performance of damage monitoring via the SA sensor in the PSC anchorage.

Correction: Pham et al. Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model. Sensors 2020, 20, 3382.

The authors wish to make the following correction to this paper [...].

Piezoelectric Sensor-Embedded Smart Rock for Damage Monitoring in a Prestressed Anchorage Zone.

In this paper, a piezoelectric sensor-embedded smart rock is proposed for the electromechanical impedance monitoring of internal concrete damage in a prestressed anchorage zone. Firstly, a piezoelectric sensor-embedded smart rock is analyzed for impedance monitoring in concrete structures. An impedance measurement model is analyzed for the PZT (lead zirconate titanate)-embedded smart rock under compression in a concrete member. Secondly, a prototype of the smart rock embedded with a PZT sensor is designed in order to ascertain, sensitively, the variations of the impedance signatures induced by concrete damage in an anchorage zone. Thirdly, the performance of the smart rock is estimated from a numerical analysis and experimental tests. Variations in the impedance signals under compressive test cases are analyzed in order to predetermine the sensitive frequency band for the impedance monitoring. Lastly, an experiment on an anchorage zone embedded with the smart rocks and surface-mounted PZT sensors is conducted for the impedance measurement under a series of loading cases. The impedance variations are quantified in order to comparatively evaluate the feasibility of the sensor-embedded smart rock for the detection of internal concrete damage in the anchorage zone. The results show that the internal concrete damage was successfully detected using the PZT-embedded smart rock, thus enabling the application of the technique for anchorage zone health monitoring.

N-acetylcysteine reduce the stress induced by cold storage of platelets: A potential way to extend shelf life of platelets.

The room temperature storage used for platelets worldwide leads to platelet storage lesion (PSL) and risk of bacterial growth, limiting platelet shelf life and safety in transfusion. Thus, there is a need for an alternative storage method that can serve as effective temperature storage for platelet concentrates (PCs). In the previous investigation, we have shown that N-acetylcysteine (NAC) is a potential candidate for an additive solution to retain platelet characteristics during cold storage for up to 5 days. However, the study partially describes the efficacy and has drawbacks to address. Here, we used the apheresis platelet product with 50 mM NAC and stored up to 10 days under refrigerated condition (4 ± 1 °C). Stored platelet concentrates were analyzed for critical parameters such as platelet activation, annexin V binding, sialic acid, reactive oxygen species (ROS), neuraminidase activity, and in vivo efficacy using Prkdcscid mice. Investigation observations revealed that PCs with NAC showed reduced platelet activation, annexin V binding, ROS production, and sialic acid levels. in vivo recovery of PCs showed similar recovery rates stored PCs irrespective of treatment or storage condition. However, on the tenth day after 24 h, recovery in room temperature stored concentrates was about 32 %, whereas in NAC treated refrigerated concentrates, it stands at 47 %. These observations indicate that NAC addition protects refrigerated concentrates during long-term storage retaining the platelet integrity. The study also suggests that extending PC storage beyond 10 days is practically accomplishable with efficacy similar to room temperature (RT) stored PCs.

Monitoring of Corroded and Loosened Bolts in Steel Structures via Deep Learning and Hough Transforms.

In this study, a regional convolutional neural network (RCNN)-based deep learning and Hough line transform (HLT) algorithm are applied to monitor corroded and loosened bolts in steel structures. The monitoring goals are to detect rusted bolts distinguished from non-corroded ones and also to estimate bolt-loosening angles of the identified bolts. The following approaches are performed to achieve the goals. Firstly, a RCNN-based autonomous bolt detection scheme is designed to identify corroded and clean bolts in a captured image. Secondly, a HLT-based image processing algorithm is designed to estimate rotational angles (i.e., bolt-loosening) of cropped bolts. Finally, the accuracy of the proposed framework is experimentally evaluated under various capture distances, perspective distortions, and light intensities. The lab-scale monitoring results indicate that the suggested method accurately acquires rusted bolts for images captured under perspective distortion angles less than 15° and light intensities larger than 63 lux.

Comparing recombinant MPB70/SahH and native 20-kDa protein for detecting bovine tuberculosis using ELISA.

Bovine tuberculosis (bTB) is a zoonosis caused by Mycobacterium bovis. Test-and-cull protocols and gross pathological examinations of abattoir animals as well as milk pasteurization have been implemented to prevent the spread of tuberculosis from animals to humans worldwide. Despite the importance of precise and rapid diagnostic tests, conventional methods including intradermal skin tests and γ-interferon assays are limited by the high rate of false-negative results for cattle in the late infectious stage and due to laborious and time-consuming procedures. Therefore, antibody detection methods such as enzyme-linked immunosorbent assay (ELISA) are urgently needed to supplement the established approaches and expand the diagnostic window. This study was conducted to develop a bTB ELISA by evaluating recombinant and native proteins and various assay parameters. We produced recombinant MPB70 and SahH (M70S) and a native 20-kDa protein (20K) and optimized the ELISA protocol. The 20K ELISA showed 94.4% sensitivity and 98.2% specificity with an optimal sample-to-positive ratio cut-off of 0.531. The sensitivity and specificity of M70S ELISA were 94.4% and 97.3%, respectively, with an optimal sample-to-negative ratio cut-off of 1.696. Both assays showed acceptable diagnostic efficiency and could be used for bTB diagnosis in combination with established methods for herd screening and to expand the diagnostic window.

Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model.

In this study, we investigate a novel idea of using synthetic images of bolts which are generated from a graphical model to train a deep learning model for loosened bolt detection. Firstly, a framework for bolt-loosening detection using image-based deep learning and computer graphics is proposed. Next, the feasibility of the proposed framework is demonstrated through the bolt-loosening monitoring of a lab-scaled bolted joint model. For practicality, the proposed idea is evaluated on the real-scale bolted connections of a historical truss bridge in Danang, Vietnam. The results show that the deep learning model trained by the synthesized images can achieve accurate bolt recognitions and looseness detections. The proposed methodology could help to reduce the time and cost associated with the collection of high-quality training data and further accelerate the applicability of vision-based deep learning models trained on synthetic data in practice.

Sensor Fault Diagnosis for Impedance Monitoring Using a Piezoelectric-Based Smart Interface Technique.

For a structural health monitoring (SHM) system, the operational functionality of sensors is critical for successful implementation of a damage identification process. This study presents experimental and analytical investigations on sensor fault diagnosis for impedance-based SHM using the piezoelectric interface technique. Firstly, the piezoelectric interface-based impedance monitoring is experimentally conducted on a steel bolted connection to investigate the effect of structural damage and sensor defect on electromechanical (EM) impedance responses. Based on the experimental analysis, sensor diagnostic approaches using EM impedance features are designed to distinguish the sensor defect from the structural damage. Next, a novel impedance model of the piezoelectric interface-driven system is proposed for the analytical investigation of sensor fault diagnosis. Various parameters are introduced into the EM impedance formulation to model the effect of shear-lag phenomenon, sensor breakage, sensor debonding, and structural damage. Finally, the proposed impedance model is used to analytically estimate the change in EM impedance responses induced by the structural damage and the sensor defect. The analytical results are found to be consistent with experimental observations, thus evidencing the feasibility of the novel impedance model for sensor diagnosis and structural integrity assessment. The study is expected to provide theoretical and experimental foundations for impedance monitoring practices, using the piezoelectric interface technique, with the existence of sensor faults.

Sensing Region Characteristics of Smart Piezoelectric Interface for Damage Monitoring in Plate-Like Structures.

For impedance-based damage detection practices, the sensing range of piezoelectric devices is an important parameter that should be determined before real implementations. This study presents numerical and experimental analyses for characterizing the sensing region of a smart PZT (lead⁻zirconate⁻titanate) interface for damage monitoring in plate-like structures. First, a finite element (FE) model of the PZT interface mounted on a plate structure is established. The impedance responses of the PZT interface are numerically simulated under different damage locations inflicted in the plate domain. The impedance features are extracted from the impedance signatures to analyze the sensing distance and the damage detectability of the PZT interface. Next, the splice plate of a bolted connection is selected as a practical plate-like structure for the experimental examination of the PZT interface's sensing region on a limited plate domain. The damage sensitivity behavior of the PZT interface is analyzed with respect to the damage location on the splice plate. An FE analysis of the corresponding PZT interface-splice plate system is also conducted to support the experimental results.

Local Strand-Breakage Detection in Multi-Strand Anchorage System Using an Impedance-Based Stress Monitoring Method-Feasibility Study.

This study investigates the feasibility of impedance-based stress monitoring method for local-strand breakage detection in multi-strand anchorage systems. Firstly, stress fields of a multi-strand anchorage system are numerically analyzed to examine anchorage's responses sensitive to local strand breakage. Secondly, an impedance-based stress monitoring technique via the PZT interface is outlined. Thirdly, a novel hoop-type PZT interface is designed for the multi-strands anchorage to monitor the stress variation induced by the strand breakage. Local dynamic responses of the hoop-type PZT interface are analyzed to predetermine the effective frequency ranges. Finally, the numerical feasibility of the proposed method is verified on a seven-strand anchorage system under various strand breakage cases. Variations in impedance responses are statistically quantified, and broken strands are localized by linear tomography analysis of damage indices. A lab-scale experiment is also conducted on a multi-strands anchorage to evaluate the realistic performance of the hoop PZT interface for impedance-based stress monitoring method.