Implanting colloidal nanoparticles into single-crystalline zeolites for Catalytic Dehydration.

The encapsulation of functional colloidal nanoparticles (100 nm) into single-crystalline ZSM-5 zeolites, aiming to create uniform core-shell structures, is a highly sought-after yet formidable objective due to significant lattice mismatch and distinct crystallization properties. In this study, we demonstrate the fabrication of a core-shell structured single-crystal zeolite encompassing an Fe3O4 colloidal core via a novel confinementstepwise crystallization methodology. By engineering a confined nanocavity, anchoring nucleation sites, and executing stepwise crystallization, we have successfully encapsulated colloidal nanoparticles (CN) within single-crystal zeolites. These grafted sites, alongside the controlled crystallization process, compel the zeolite seed to nucleate and expand along the Fe3O4 colloidal nanoparticle surface, within a meticulously defined volume (1.5 × 107 ≤ V ≤ 1.3 × 108 nm3). Our strategy exhibits versatility and adaptability to an array of zeolites, including but not restricted to ZSM-5, NaA, ZSM-11, and TS-1 with polycrystalline zeolite shell. We highlight the uniformly structured magnetic-nucleus single-crystalline zeolite, which displays pronounced superparamagnetism (14 emu/g) and robust acidity (~0.83 mmol/g). This innovative material has been effectively utilized in a magnetically stabilized bed (MSB) reactor for the dehydration of ethanol, delivering an exceptional conversion rate (98%), supreme ethylene selectivity (98%), and superior catalytic endurance (in excess of 100 hours).

Predicting Quercus gilva distribution dynamics and its response to climate change induced by GHGs emission through MaxEnt modeling.

Quercus gilva, an evergreen tree species in Quercus section Cyclobalanopsis, is an ecologically and economically valuable species in subtropical regions of East Asia. Predicting the impact of climate change on potential distribution of Q. gilva can provide a scientific basis for the conservation and utilization of its genetic resources, as well as for afforestation. In this study, 74 distribution records of Q. gilva and nine climate variables were obtained after data collection and processing. Current climate data downloaded from WorldClim and future climate data predicted by four future climate scenarios (2040s SSP1-2.6, 2040s SSP5-8.5, 2060s SSP1-2.6, and 2060s SSP5-8.5) mainly based on greenhouse gases emissions of distribution sites were used in MaxEnt model with optimized parameters to predict distribution dynamics of Q. gilva and its response to climate change. The results showed that the predicted current distribution was consistent with natural distribution of Q. gilva, which was mainly located in Hunan, Jiangxi, Zhejiang, Fujian, Guizhou, and Taiwan provinces of China, as well as Japan and Jeju Island of South Korea. Under current climate conditions, precipitation factors played a more significant role than temperature factors on distribution of Q. gilva, and precipitation of driest quarter (BIO17) is the most important restriction factor for its current distribution (contribution rate of 57.35%). Under future climate conditions, mean temperature of driest quarter (BIO9) was the essential climate factor affecting future change in potential distribution of Q. gilva. As the degree of climatic anomaly increased in the future, the total area of predicted distribution of Q. gilva showed a shrinking trend (decreased by 12.24%-45.21%) and Q. gilva would migrate to high altitudes and latitudes. The research results illustrated potential distribution range and suitable climate conditions of Q. gilva, which can provide essential theoretical references for the conservation, development, and utilization of Q. gilva and other related species.

HIMS-Net: Horizontal-vertical interaction and multiple side-outputs network for cyst segmentation in jaw images.

Jaw cysts are mainly caused by abnormal tooth development, chronic oral inflammation, or jaw damage, which may lead to facial swelling, deformity, tooth loss, and other symptoms. Due to the diversity and complexity of cyst images, deep-learning algorithms still face many difficulties and challenges. In response to these problems, we present a horizontal-vertical interaction and multiple side-outputs network for cyst segmentation in jaw images. First, the horizontal-vertical interaction mechanism facilitates complex communication paths in the vertical and horizontal dimensions, and it has the ability to capture a wide range of context dependencies. Second, the feature-fused unit is introduced to adjust the network's receptive field, which enhances the ability of acquiring multi-scale context information. Third, the multiple side-outputs strategy intelligently combines feature maps to generate more accurate and detailed change maps. Finally, experiments were carried out on the self-established jaw cyst dataset and compared with different specialist physicians to evaluate its clinical usability. The research results indicate that the Matthews correlation coefficient (Mcc), Dice, and Jaccard of HIMS-Net were 93.61, 93.66 and 88.10% respectively, which may contribute to rapid and accurate diagnosis in clinical practice.

Identification of dihydroquinolizinone derivatives with nitrogen heterocycle moieties as new anti-HBV agents.

The sustained loss of HBsAg is considered a pivotal indicator for achieving functional cure of HBV. Dihydroquinolizinone derivatives (DHQs) have demonstrated remarkable inhibitory activity against HBsAg both in vitro and in vivo. However, the reported neurotoxicity associated with RG7834 has raised concerns regarding the development of DHQs. In this study, we designed and synthesized a series of DHQs incorporating nitrogen heterocycle moieties. Almost all of these compounds exhibited potent inhibition activity against HBsAg, with IC50 values at the nanomolar level. Impressively, the compound (S)-2a (10 µM) demonstrated a comparatively reduced impact on the neurite outgrowth of HT22 cells and isolated mouse DRG neurons in comparison to RG7834, thereby indicating a decrease in neurotoxicity. Furthermore, (S)-2a exhibited higher drug exposures than RG7834. The potent anti-HBV activity, reduced neurotoxicity, and favorable pharmacokinetic profiles underscore its promising potential as a lead compound for future anti-HBV drug discovery.

Ordered Mesoporous Crystalline Frameworks Toward Promising Energy Applications.

Ordered mesoporous crystalline frameworks (MCFs), which possess both functional frameworks and well-defined porosity, receive considerable attention because of their unique properties including high surface areas, large pore sizes, tailored porous structures, and compositions. Construction of novel crystalline mesoporous architectures that allows for rich accessible active sites and efficient mass transfer is envisaged to offer ample opportunities for potential energy-related applications. In this review, the rational synthesis, unique structures, and energy applications of MCFs are the main focus. After summarizing the synthetic approaches, an emphasis is placed on the delicate control of crystallites, mesophases, and nano-architectures by concluding basic principles and showing representative examples. Afterward, the currently fabricated components of MCFs such as metals, metal oxides, metal sulfides, and metal-organic frameworks are described in sequence. Further, typical applications of MCFs in rechargeable batteries, supercapacitors, electrocatalysis, and photocatalysis are highlighted. This review ends with the possible development and synthetic challenges of MCFs as well as a future prospect for high-efficiency energy applications, which underscores a pathway for developing advanced materials.

Mesoporous TiO2 Single-Crystal Particles from Controlled Crystallization-Driven Mono-Micelle Assembly as an Efficient Photocatalyst.

Mesoporous materials with crystalline frameworks have been widely explored in many fields due to their unique structure and crystalline feature, but accurate manipulations over crystalline scaffolds, mainly composed of uncontrolled polymorphs, are still lacking. Herein, we explored a controlled crystallization-driven monomicelle assembly approach to construct a type of uniform mesoporous TiO2 particles with atomically aligned single-crystal frameworks. The resultant mesoporous TiO2 single-crystal particles possess an angular shape ∼80 nm in diameter, good mesoporosity (a high surface area of 112 m2 g-1 and a mean pore size at 8.3 nm), and highly oriented anatase frameworks. By adjusting the evaporation rate during assembly, such a facile solution-processed strategy further enables the regulation of the particle size and mesopore size without the destruction of the oriented crystallites. Such a combination of ordered mesoporosity and crystalline orientation provides both effective mass and charge transportation, leading to a significant increase in the hydrogen generation rate. A maximum hydrogen evolution rate of 12.5 mmol g-1 h-1 can be realized, along with great stability under solar light. Our study is envisaged to extend the possibility of mesoporous single crystal growth to a range of functional ceramics and semiconductors toward advanced applications.

Boosted Oxygen Kinetics of Hierarchically Mesoporous Mo2 C/C for High-current-density Zn-air Battery.

The high-current-density Zn-air battery shows big prospects in next-generation energy technologies, while sluggish O2 reaction and diffusion kinetics barricade the applications. Herein, the sequential assembly is innovatively demonstrated for hierarchically mesoporous molybdenum carbides/carbon microspheres with a tunable thickness of mesoporous carbon layers (Meso-Mo2 C/C-x, where x represents the thickness). The optimum Meso-Mo2 C/C-14 composites (≈2 µm in diameter) are composed of mesoporous nanosheets (≈38 nm in thickness), which possess bilateral mesoporous carbon layers (≈14 nm in thickness), inner Mo2 C/C layers (≈8 nm in thickness) with orthorhombic Mo2 C nanoparticles (≈2 nm in diameter), a high surface area of ≈426 m2  g-1 , and open mesopores (≈6.9 nm in size). Experiments and calculations corroborate the hierarchically mesoporous Mo2 C/C can enhance hydrophilicity for supplying sufficient O2 , accelerate oxygen reduction kinetics by highly-active Mo2 C and N-doped carbon sites, and facilitate O2 diffusion kinetics over hierarchically mesopores. Therefore, Meso-Mo2 C/C-14 outputs a high half-wave potential (0.88 V vs RHE) with a low Tafel slope (51 mV dec-1 ) for oxygen reduction. More significantly, the Zn-air battery delivers an ultrahigh power density (272 mW cm-2 ), and an unprecedented 100 h stability at a high-current-density condition (100 mA cm-2 ), which is one of the best performances.

Construction of Type-II Heterojunctions in Crystalline Carbon Nitride for Efficient Photocatalytic H2 Evolution.

As an encouraging photocatalyst, crystalline carbon nitride (CCN) exhibits unsatisfactory photocatalytic activity and stability due to its rapid recombination of photo-generative carriers. Herein, high-crystalline g-C3N4 was prepared, including CCN obtained in KCl (K-CCN), LiCl-KCl mixture (Li/K-CCN), and LiCl-NaCl-KCl mixture (Li/Na/K-CCN), via the molten salt strategy using pre-prepared bulk carbon nitride (BCN) as a precursor. The obtained BCN sample was formed by heptazine-based units, which convert into triazine-based units for K-CCN. Heptazine and triazine are two isotypes that co-exist in the Li/K-CCN and Li/Na/K-CCN samples. Compared with BCN and other CCN samples, the as-prepared Li/Na/K-CCN sample exhibited the optimal photocatalytic hydrogen evolution rates (3.38 mmol·g-1·h-1 under simulated sunlight and 2.25 mmol·g-1·h-1 under visible light) and the highest apparent quantum yield (10.97%). The improved photocatalytic performance of the Li/Na/K-CCN sample is mainly attributed to the construction of type-II heterojunction and the institution of the built-in electric field between triazine-based CCN and heptazine-based BCN. This work provides a new strategy for the structural optimization and heterostructure construction of crystalline carbon nitride photocatalysts.

BiDFDC-Net: a dense connection network based on bi-directional feedback for skin image segmentation.

Accurate segmentation of skin lesions in dermoscopic images plays an important role in improving the survival rate of patients. However, due to the blurred boundaries of pigment regions, the diversity of lesion features, and the mutations and metastases of diseased cells, the effectiveness and robustness of skin image segmentation algorithms are still a challenging subject. For this reason, we proposed a bi-directional feedback dense connection network framework (called BiDFDC-Net), which can perform skin lesions accurately. Firstly, under the framework of U-Net, we integrated the edge modules into each layer of the encoder which can solve the problem of gradient vanishing and network information loss caused by network deepening. Then, each layer of our model takes input from the previous layer and passes its feature map to the densely connected network of subsequent layers to achieve information interaction and enhance feature propagation and reuse. Finally, in the decoder stage, a two-branch module was used to feed the dense feedback branch and the ordinary feedback branch back to the same layer of coding, to realize the fusion of multi-scale features and multi-level context information. By testing on the two datasets of ISIC-2018 and PH2, the accuracy on the two datasets was given by 93.51% and 94.58%, respectively.

Group theoretic particle swarm optimization for gray-level medical image enhancement.

As a principal category in the promising field of medical image processing, medical image enhancement has a powerful influence on the intermedia features and final results of the computer aided diagnosis (CAD) system by increasing the capacity to transfer the image information in the optimal form. The enhanced region of interest (ROI) would contribute to the early diagnosis and the survival rate of patients. Meanwhile, the enhancement schema can be treated as the optimization approach of image grayscale values, and metaheuristics are adopted popularly as the mainstream technologies for medical image enhancement. In this study, we propose an innovative metaheuristic algorithm named group theoretic particle swarm optimization (GT-PSO) to tackle the optimization problem of image enhancement. Based on the mathematical foundation of symmetric group theory, GT-PSO comprises particle encoding, solution landscape, neighborhood movement and swarm topology. The corresponding search paradigm takes place simultaneously under the guidance of hierarchical operations and random components, and it could optimize the hybrid fitness function of multiple measurements of medical images and improve the contrast of intensity distribution. The numerical results generated from the comparative experiments show that the proposed GT-PSO has outperformed most other methods on the real-world dataset. The implication also indicates that it would balance both global and local intensity transformations during the enhancement process.

Stepwise Monomicelle Assembly for Highly Ordered Mesoporous TiO2 Membranes with Precisely Tailored Mesophase and Porosity.

Mesoporous materials with crystalline frameworks have been acknowledged as very attractive materials in various applications. Nevertheless, due to the cracking issue during crystallization and incompatible hydrolysis and assembly, the precise control for crystalline mesoscale membranes is quite infertile. Herein, we presented an ingenious stepwise monomicelle assembly route for the syntheses of highly ordered mesoporous crystalline TiO2 membranes with delicately controlled mesophase, mesoporosity, and thickness. Such a process involves the preparation of monomicelle hydrogels and follows self-assembly by stepwise solvent evaporation, which enables the sensitive hydrolysis of TiO2 oligomers and dilatory micelle assembly to be united. In consequence, the fabricated mesoporous TiO2 membranes exhibit a broad flexibility, including tunable ordered mesophases (worm-like, hexagonal p6mm to body-centered cubic Im3̅m), controlled mesopore sizes (3.0-8.0 nm), and anatase grain sizes (2.3-8.4 nm). Besides, such mesostructured crystalline TiO2 membranes can be extended to diverse substrates (Ti, Ag, Si, FTO) with tailored thickness. The great mesoporosity of the in situ fabricated mesoscopic membranes also affords excellent pseudocapacitive behavior for sodium ion storage. This study underscores a novel pathway for balancing the interaction of precursors and micelles, which could have implications for synthesizing crystalline mesostructures in higher controllability.

The Effect of Lifting-and-Thrusting Laser Acupuncture on Electrodermal Activity of Acupoints, Pulse Characteristics, and Brainwave.

Acupuncture has been shown as an effective traditional Chinese medicine treatment method, especially for pain relief. Recently, laser acupuncture is becoming increasingly popular, thanks to its noninvasive and painless nature and effectiveness in treating diseases, proven by many studies (for example, some previous studies showed that low-power laser stimulation is able to increase the power of alpha rhythms and theta waves). In our prior work, we developed a novel laser acupuncture model that emulates lifting-and-thrusting operation commonly used in traditional needle acupuncture and showed its benefit in improving cardiac output and peripheral circulation. By extending our previous studies, in this work, we perform extensive experiments to understand the effect of such a system on electrodermal activity (EDA) of acupoints, pulse characteristics, and brainwave, to further verify its efficacy. In particular, we found that laser stimulation could cause significant changes in EDA of acupoints, pulse amplitude, pulse-rate-variability (PRV), and acupoint conductance, as a function of laser power and stimulation time. In addition, laser acupuncture with the lifting-and-thrusting operation has more significant effect on increasing the power of alpha and theta frequency bands as compared to laser acupuncture without the lifting-and-thrusting operation. Finally, given sufficient stimulation time (e.g., > 20 min), the performance of a low-powered laser acupuncture with the lifting-and-thrusting operation could be comparable to that of traditional needle acupuncture.

Reflex Auriculo-Cardiac (RAC) Induced by Auricular Laser and Needle Acupuncture: New Case Results Using a Smartphone.

The reflex auriculo-cardiac (RAC), dynamic pulse reaction (Nogier reflex), or vascular autonomic signal was proposed by Nogier. It refers to the pulse changes that can occur in the radial artery immediately after auricular acupuncture is performed. RAC is helpful for the clinical practice of auricular acupuncture, but there is a lack of objective verification methods. Photoplethysmography (PPG) has been used to objectively calculate radial artery blood flow. This study used PPG via a smartphone to measure RAC induced by auricular acupuncture. Thirty subjects without major diseases were recruited to receive traditional needle and laser acupuncture. The Shen Men ear point and control points were stimulated for 20 s. PPG was continuously measured during the acupuncture. The PPG data were tested for differences with a paired t-test. The results showed that there were no statistical differences in the frequency and amplitude of PPG obtained before and after acupuncture, either with a traditional needle or laser acupuncture. However, interestingly, it was found that one patient with insomnia, one patient with viral respiratory symptoms, and two menstruating females exhibited changes in PPG within five seconds of needle placement. We hypothesized that RAC might be induced by auricular acupuncture and could be quantified by PPG, even among subjects suffering from mild diseases; however, auricular acupuncture might not induce a measurable RAC in totally healthy subjects.

Group theoretic particle swarm optimization for multi-level threshold lung cancer image segmentation.

Background: Image segmentation is an important step during the processing of medical images. For example, for the computer aid diagnostic systems for lung cancer image analysis, the segmented regions of tumors would help doctors in early diagnosis to determine timely and appropriate treatment possibilities and thereby improve the survival rate of the patients. However, general clinical routines of manual segmentation for large number of medical images are very difficult and time consuming, which is the challenge we aim to tackle using our proposed method. Methods: A novel image segmentation method with evolutionary learning technique named Group Theoretic Particle Swarm Optimization is proposed. It can tackle multi-level thresholding optimization problem during the segmentation process and rebuild the search paradigm according to the solid mathematical foundation of symmetric group from four designable aspects, which are particle encoding, solution landscape, neighborhood movement and swarm topology, respectively. The Kapur's entropy of multi-level thresholds is assessed as the objective function. Results: In contrast to those conventional metaheuristics methods for lung cancer image segmentation, this newly presented method generates the best performance result among them. Experimental results show that its Kapur's entropy has the value of 9.07, which is 16% higher than the worst case. Computational time is acceptable at the cost of 173.730 seconds, average level of evaluation metrics [Kappa, Precision, Recall, F1-measure, intersection over union (IoU) and receiver operating characteristic (ROC)] is over 90%, and search process of multi-level threshold combination would finally converge in the later phase of iterations after 700. The ablation study indicates that all components are significant to the contributions of our proposed method. Conclusions: Group Theoretic Particle Swarm Optimization for multi-level threshold segmentation is an efficient way to split a medical image into distinct regions and extract tumor tissues regions from the background. It maintains the balanced relationship between diversification and intensification during the search process and helps clinicians to make the diagnosis more accurately. Our proposed method processes potential medical value and clinical meanings.

Surface-redox sodium-ion storage in anatase titanium oxide.

Sodium-ion storage technologies are promising candidates for large-scale grid systems due to the abundance and low cost of sodium. However, compared to well-understood lithium-ion storage mechanisms, sodium-ion storage remains relatively unexplored. Herein, we systematically determine the sodium-ion storage properties of anatase titanium dioxide (TiO2(A)). During the initial sodiation process, a thin surface layer (~3 to 5 nm) of crystalline TiO2(A) becomes amorphous but still undergoes Ti4+/Ti3+ redox reactions. A model explaining the role of the amorphous layer and the dependence of the specific capacity on the size of TiO2(A) nanoparticles is proposed. Amorphous nanoparticles of ~10 nm seem to be optimum in terms of achieving high specific capacity, on the order of 200 mAh g-1, at high charge/discharge rates. Kinetic studies of TiO2(A) nanoparticles indicate that sodium-ion storage is due to a surface-redox mechanism that is not dependent on nanoparticle size in contrast to the lithiation of TiO2(A) which is a diffusion-limited intercalation process. The surface-redox properties of TiO2(A) result in excellent rate capability, cycling stability and low overpotentials. Moreover, tailoring the surface-redox mechanism enables thick electrodes of TiO2(A) to retain high rate properties, and represents a promising direction for high-power sodium-ion storage.

Improved U-Net based on cross-layer connection for pituitary adenoma MRI image segmentation.

Pituitary adenoma is a common neuroendocrine neoplasm, and most of its MR images are characterized by blurred edges, high noise and similar to surrounding normal tissues. Therefore, it is extremely difficult to accurately locate and outline the lesion of pituitary adenoma. To sovle these limitations, we design a novel deep learning framework for pituitary adenoma MRI image segmentation. Under the framework of U-Net, a newly cross-layer connection is introduced to capture richer multi-scale features and contextual information. At the same time, full-scale skip structure can reasonably utilize the above information obtained by different layers. In addition, an improved inception-dense block is designed to replace the classical convolution layer, which can enlarge the effectiveness of the receiving field and increase the depth of our network. Finally, a novel loss function based on binary cross-entropy and Jaccard losses is utilized to eliminate the problem of small samples and unbalanced data. The sample data were collected from 30 patients in Quzhou People's Hospital, with a total of 500 lesion images. Experimental results show that although the amount of patient sample is small, the proposed method has better performance in pituitary adenoma image compared with existing algorithms, and its Dice, Intersection over Union (IoU), Matthews correlation coefficient (Mcc) and precision reach 88.87, 80.67, 88.91 and 97.63%, respectively.

Modified UNet++ with atrous spatial pyramid pooling for blood cell image segmentation.

Blood cell image segmentation is an important part of the field of computer-aided diagnosis. However, due to the low contrast, large differences in cell morphology and the scarcity of labeled images, the segmentation performance of cells cannot meet the requirements of an actual diagnosis. To address the above limitations, we present a deep learning-based approach to study cell segmentation on pathological images. Specifically, the algorithm selects UNet++ as the backbone network to extract multi-scale features. Then, the skip connection is redesigned to improve the degradation problem and reduce the computational complexity. In addition, the atrous spatial pyramid pooling (ASSP) is introduced to obtain cell image information features from each layer through different receptive domains. Finally, the multi-sided output fusion (MSOF) strategy is utilized to fuse the features of different semantic levels, so as to improve the accuracy of target segmentation. Experimental results on blood cell images for segmentation and classification (BCISC) dataset show that the proposed method has significant improvement in Matthew's correlation coefficient (Mcc), Dice and Jaccard values, which are better than the classical semantic segmentation network.

Preparation, characteristics and cytotoxicity of green synthesized selenium nanoparticles using Paenibacillus motobuensis LY5201 isolated from the local specialty food of longevity area.

Selenium is an essential micronutrient element. For the extremely biotoxic of selenite, Selenium nanoparticles (SeNPs) is gaining increasing interest. In this work, a selenium-enriched strain with highly selenite-resistant (up to 173 mmol/L) was isolated from the local specialty food of longevity area and identified as Paenibacillus motobuensis (P. motobuensis) LY5201. Most of the SeNPs were accumulated extracellular. SeNPs were around spherical with a diameter of approximately 100 nm. The X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy showed that the purified SeNPs consisted of selenium and proteins. Our results suggested that P. motobuensis LY5201could be a suitable and robust biocatalyst for SeNPs synthesis. In addition, the cytotoxicity effect and the anti-invasive activity of SeNPs on the HepG2 showed an inhibitory effect on HepG2, indicating that SeNPs could be used as a potential anticancer drug.

A soft sensing method of billet surface temperature based on ILGSSA-LSSVM.

It is difficult to measure the surface temperature of continuous casting billet, which results in the lack of important feedback parameters for further scientific control of the billet quality. This paper proposes a sparrow search algorithm to optimize the Least Square Support Vector Machine (LSSVM) model for surface temperature prediction of the billet, which is further improved by Logistic Chaotic Mapping and Golden Sine Algorithm (Improve Logistic Golden Sine Sparrow Search Algorithm LSSVM, short name ILGSSA-LSSVM). Using the Improved Logistic Chaos Mapping and Golden Sine Algorithm to find the optimal initial sparrow population, the value of penalty factor [Formula: see text] and kernel parameter [Formula: see text] for LSSVM are calculated. Global optimization method is adopted to find the optimal parameter combination, so that the negative influence of randomly initializing parameters on the prediction accuracy would be reduced. Our proposed ILGSSA-LSSVM soft sensing model is compared respectively with traditional Least Square Support Vector Machine, BP neural network and Gray Wolf optimized Least Square Support Vector Machine, results show that proposed model outperformed the others. Experiments show that the maximum error of ILGSA-LSSVM soft sensing model is 3.85733 °C, minimum error is 0.0174 °C, average error is 0.05805 °C, and generally outperformed other comparison models.

Fabrication and Experimental Validation of a Sensitive and Robust Tactile Sensing Array with a Micro-Structured Porous Dielectric Layer.

The development of pressure sensors of high sensitivity and stable robustness over a broad range is indispensable for the future progress of electronic skin applicable to the detection of normal and shear pressures of various dynamic human motions. Herein, we present a flexible capacitive tactile sensing array that incorporates a porous dielectric layer with micro-patterned structures on the surface to enable the sensitive detection of normal and shear pressures. The proposed sensing array showed great pressure-sensing performance in the experiments, with a broad sensing range from several kPa to 150 kPa of normal pressure and 20 kPa of shear pressure. Sensitivities of 0.54%/kPa at 10 kPa and below, 0.45%/kPa between 10 kPa and 80 kPa, and 0.12%/kPa at 80 kPa and above were achieved for normal pressures. Meanwhile, for shear pressures, sensitivities up to 1.14%/kPa and 1.08%/kPa in x and y directions, respectively, and below 10 kPa, 0.73%/kPa, and 0.75%/kPa under shear pressure over 10 kPa were also validated. The performance of the finger-attached sensing array was also demonstrated, demonstrating which was a potential electronic skin to use in all kinds of wearable devices, including prosthetic hands, surgical robots, and other pressure monitoring systems.