Sulfidation-reoxidation enhances heavy metal immobilization by vivianite.

Iron minerals in nature are pivotal hosts for heavy metals, significantly influencing their geochemical cycling and eventual fate. It is generally accepted that, vivianite, a prevalent iron phosphate mineral in aquatic and terrestrial environments, exhibits a limited capacity for adsorbing cationic heavy metals. However, our study unveils a remarkable phenomenon that the synergistic interaction between sulfide (S2-) and vivianite triggers an unexpected sulfidation-reoxidation process, enhancing the immobilization of heavy metals such as cadmium (Cd), copper (Cu), and zinc (Zn). For instance, the combination of vivianite and S2- boosted the removal of Cd2+ from the aqueous phase under anaerobic conditions, and ensured the retention of Cd stabilized in the solid phase when shifted to aerobic conditions. It is intriguing to note that no discrete FeS formation was detected in the sulfidation phase, and the primary crystal structure of vivianite largely retained its integrity throughout the whole process. Detailed molecular-level investigations indicate that sulfidation predominantly targets the Fe(II) sites at the corners of the PO4 tetrahedron in vivianite. With the transition to aerobic conditions, the exothermic oxidation of CdS and the S sites in vivianite initiates, rendering it thermodynamically favorable for Cd to form multidentate coordination structures, predominantly through the Cd-O-P and Cd-O-Fe bonds. This mechanism elucidates how Cd is incorporated into the vivianite structure, highlighting a novel pathway for heavy metal immobilization via the sulfidation-reoxidation dynamics in iron phosphate minerals.

Evaluation of the Alveolar Crest and Cemento-Enamel Junction in Periodontitis Using Object Detection on Periapical Radiographs.

The severity of periodontitis can be analyzed by calculating the loss of alveolar crest (ALC) level and the level of bone loss between the tooth's bone and the cemento-enamel junction (CEJ). However, dentists need to manually mark symptoms on periapical radiographs (PAs) to assess bone loss, a process that is both time-consuming and prone to errors. This study proposes the following new method that contributes to the evaluation of disease and reduces errors. Firstly, innovative periodontitis image enhancement methods are employed to improve PA image quality. Subsequently, single teeth can be accurately extracted from PA images by object detection with a maximum accuracy of 97.01%. An instance segmentation developed in this study accurately extracts regions of interest, enabling the generation of masks for tooth bone and tooth crown with accuracies of 93.48% and 96.95%. Finally, a novel detection algorithm is proposed to automatically mark the CEJ and ALC of symptomatic teeth, facilitating faster accurate assessment of bone loss severity by dentists. The PA image database used in this study, with the IRB number 02002030B0 provided by Chang Gung Medical Center, Taiwan, significantly reduces the time required for dental diagnosis and enhances healthcare quality through the techniques developed in this research.

Encapsulation of Sn Sub-nanoclusters in Multichannel Carbon Matrix for High-Performance Potassium-ion Batteries.

Sub-nanoclusters with ultra-small particle sizes are particularly significant to create advanced energy storage materials. Herein, Sn sub-nanoclusters encapsulated in nitrogen-doped multichannel carbon matrix (denoted as Sn-SCs@MCNF) are designed by a facile and controllable route as flexible anode for high-performance potassium ion batteries (PIBs). The uniformly dispersed Sn sub-nanoclusters in multichannel carbon matrix can be precisely identified, which ensure us to clarify the size influence on the electrochemical performance. The sub-nanoscale effect of Sn-SCs@MCNF restrains electrode pulverization and enhances the K+ diffusion kinetics, leading to the superior cycling stability and rate performance. As freestanding anode in PIBs, Sn-SCs@MCNF manifests superior K+ storage properties, such as exceptional cycling stability (331 mAh g-1 after 150 cycles at 100 mA g-1) and rate capability. Especially, the Sn-SCs@MCNF||KFe[Fe(CN)6] full cell demonstrates impressive reversible capacity of 167 mAh g-1 at 0.4 A g-1 even after 200 cycles. Theoretical calculations clarify that the ultrafine Sn sub-nanoclusters are beneficial for electron transfer and contribute to the lower energy barriers of the intermediates, thereby resulting in promising electrochemical performance. Comprehensive investigation for the intrinsic K+ storage process of Sn-SCs@MCNF is revealed by in situ analysis. This work provides vital guidance to design sub-nanoscale functional materials for high-performance energy-storage devices.

Auxiliary Diagnosis of Dental Calculus Based on Deep Learning and Image Enhancement by Bitewing Radiographs.

In the field of dentistry, the presence of dental calculus is a commonly encountered issue. If not addressed promptly, it has the potential to lead to gum inflammation and eventual tooth loss. Bitewing (BW) images play a crucial role by providing a comprehensive visual representation of the tooth structure, allowing dentists to examine hard-to-reach areas with precision during clinical assessments. This visual aid significantly aids in the early detection of calculus, facilitating timely interventions and improving overall outcomes for patients. This study introduces a system designed for the detection of dental calculus in BW images, leveraging the power of YOLOv8 to identify individual teeth accurately. This system boasts an impressive precision rate of 97.48%, a recall (sensitivity) of 96.81%, and a specificity rate of 98.25%. Furthermore, this study introduces a novel approach to enhancing interdental edges through an advanced image-enhancement algorithm. This algorithm combines the use of a median filter and bilateral filter to refine the accuracy of convolutional neural networks in classifying dental calculus. Before image enhancement, the accuracy achieved using GoogLeNet stands at 75.00%, which significantly improves to 96.11% post-enhancement. These results hold the potential for streamlining dental consultations, enhancing the overall efficiency of dental services.

An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System.

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the Controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle's onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle's onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems.

Heterostructured Bimetallic MOF-on-MOF Architectures for Efficient Oxygen Evolution Reaction.

Electron modulation presents a captivating approach to fabricate efficient electrocatalysts for the oxygen evolution reaction (OER), yet it remains a challenging undertaking. In this study, an effective strategy is proposed to regulate the electronic structure of metal-organic frameworks (MOFs) by the construction of MOF-on-MOF heterogeneous architectures. As a representative heterogeneous architectures, MOF-74 on MOF-274 hybrids are in situ prepared on 3D metal substrates (NiFe alloy foam (NFF)) via a two-step self-assembly method, resulting in MOF-(74 + 274)@NFF. Through a combination of spectroscopic and theory calculation, the successful modulation of the electronic property of MOF-(74 + 274)@NFF is unveiled. This modulation arises from the phase conjugation of the two MOFs and the synergistic effect of the multimetallic centers (Ni and Fe). Consequently, MOF-(74 + 274)@NFF exhibits excellent OER activity, displaying ultralow overpotentials of 198 and 223 mV at a current density of 10 mA cm-2 in the 1.0 and 0.1 M KOH solutions, respectively. This work paves the way for manipulating the electronic structure of electrocatalysts to enhance their catalytic activity.

Deep Learning for Dental Diagnosis: A Novel Approach to Furcation Involvement Detection on Periapical Radiographs.

Furcation defects pose a significant challenge in the diagnosis and treatment planning of periodontal diseases. The accurate detection of furcation involvements (FI) on periapical radiographs (PAs) is crucial for the success of periodontal therapy. This research proposes a deep learning-based approach to furcation defect detection using convolutional neural networks (CNN) with an accuracy rate of 95%. This research has undergone a rigorous review by the Institutional Review Board (IRB) and has received accreditation under number 202002030B0C505. A dataset of 300 periapical radiographs of teeth with and without FI were collected and preprocessed to enhance the quality of the images. The efficient and innovative image masking technique used in this research better enhances the contrast between FI symptoms and other areas. Moreover, this technology highlights the region of interest (ROI) for the subsequent CNN models training with a combination of transfer learning and fine-tuning techniques. The proposed segmentation algorithm demonstrates exceptional performance with an overall accuracy up to 94.97%, surpassing other conventional methods. Moreover, in comparison with existing CNN technology for identifying dental problems, this research proposes an improved adaptive threshold preprocessing technique that produces clearer distinctions between teeth and interdental molars. The proposed model achieves impressive results in detecting FI with identification rates ranging from 92.96% to a remarkable 94.97%. These findings suggest that our deep learning approach holds significant potential for improving the accuracy and efficiency of dental diagnosis. Such AI-assisted dental diagnosis has the potential to improve periodontal diagnosis, treatment planning, and patient outcomes. This research demonstrates the feasibility and effectiveness of using deep learning algorithms for furcation defect detection on periapical radiographs and highlights the potential for AI-assisted dental diagnosis. With the improvement of dental abnormality detection, earlier intervention could be enabled and could ultimately lead to improved patient outcomes.

Dynamic Electronic and Ionic Transport Actuated by Cobalt-Doped MoSe2 /rGO for Superior Potassium-Ion Batteries.

Molybdenum selenium (MoSe2 ) has tremendous potential in potassium-ion batteries (PIBs) due to its large interlayer distance, favorable bandgap, and high theoretical specific capacity. However, the poor conductivity and large K+ insertion/extraction in MoSe2 inevitably leads to sluggish reaction kinetics and poor structural stability. Herein, Coinduced engineering is employed to illuminate high-conductivity electron pathway and mobile ion diffusion of MoSe2 nanosheets anchored on reduced graphene oxide substrate (Co-MoSe2 /rGO). Benefiting from the activated electronic conductivity and ion diffusion kinetics, and an expanded interlayer spacing resulting from Co doping, combined with the interface coupling with highly conductive reduced graphene oxide (rGO) substrate through Mo-C bonding, the Co-MoSe2 /rGO anode demonstrates remarkable reversible capacity, superior rate capability, and stable long-term cyclability for potassium storage, as well as superior energy density and high power density for potassium-ion capacitors. Systematic performance measurement, dynamic analysis, in-situ/ex-situ measurements, and density functional theory (DFT) calculations elucidate the performance-enhancing mechanism of Co-MoSe2 /rGO in view of the electronic and ionic transport kinetics. This work offers deep atomic insights into the fundamental factors of electrodes for potassium-ion batteries/capacitors with superior electrochemical performance.

Coordination engineering of single zinc atoms on hierarchical dual-carbon for high-performance potassium-ion capacitors.

Dual-carbon engineering combines the advantages of graphite and hard carbon, thereby optimizing the potassium storage performance of carbon materials. However, dual-carbon engineering faces challenges balancing specific capacity, capability, and stability. In this study, we present a coordination engineering of Zn-N4 moieties on dual-carbon through additional P doping, which effectively modulates the symmetric charge distribution around the Zn center. Experimental results and theoretical calculations unveil that additional P doping induces an optimized electronic structure of the Zn-N4 moieties, thus enhancing K+ adsorption. A single-atom Zn metal coordinated with nitrogen and phosphorus reduces the K+ diffusion barrier and improves fast K+ migration kinetics. Consequently, Zn-NPC@rGO exhibits high reversible specific capacities, excellent rate capability, and impressive cycling stability, and remarkable power and energy densities for potassium-ion capacitors (PICs). This study provides insights into crucial factors for enhancing potassium storage performance.

Improving Dental Implant Outcomes: CNN-Based System Accurately Measures Degree of Peri-Implantitis Damage on Periapical Film.

As the popularity of dental implants continues to grow at a rate of about 14% per year, so do the risks associated with the procedure. Complications such as sinusitis and nerve damage are not uncommon, and inadequate cleaning can lead to peri-implantitis around the implant, jeopardizing its stability and potentially necessitating retreatment. To address this issue, this research proposes a new system for evaluating the degree of periodontal damage around implants using Periapical film (PA). The system utilizes two Convolutional Neural Networks (CNN) models to accurately detect the location of the implant and assess the extent of damage caused by peri-implantitis. One of the CNN models is designed to determine the location of the implant in the PA with an accuracy of up to 89.31%, while the other model is responsible for assessing the degree of Peri-implantitis damage around the implant, achieving an accuracy of 90.45%. The system combines image cropping based on position information obtained from the first CNN with image enhancement techniques such as Histogram Equalization and Adaptive Histogram Equalization (AHE) to improve the visibility of the implant and gums. The result is a more accurate assessment of whether peri-implantitis has eroded to the first thread, a critical indicator of implant stability. To ensure the ethical and regulatory standards of our research, this proposal has been certified by the Institutional Review Board (IRB) under number 202102023B0C503. With no existing technology to evaluate Peri-implantitis damage around dental implants, this CNN-based system has the potential to revolutionize implant dentistry and improve patient outcomes.

VLSI Design Based on Block Truncation Coding for Real-Time Color Image Compression for IoT.

It has always been a major issue for a hospital to acquire real-time information about a patient in emergency situations. Because of this, this research presents a novel high-compression-ratio and real-time-process image compression very-large-scale integration (VLSI) design for image sensors in the Internet of Things (IoT). The design consists of a YEF transform, color sampling, block truncation coding (BTC), threshold optimization, sub-sampling, prediction, quantization, and Golomb-Rice coding. By using machine learning, different BTC parameters are trained to achieve the optimal solution given the parameters. Two optimal reconstruction values and bitmaps for each 4 × 4 block are achieved. An image is divided into 4 × 4 blocks by BTC for numerical conversion and removing inter-pixel redundancy. The sub-sampling, prediction, and quantization steps are performed to reduce redundant information. Finally, the value with a high probability will be coded using Golomb-Rice coding. The proposed algorithm has a higher compression ratio than traditional BTC-based image compression algorithms. Moreover, this research also proposes a real-time image compression chip design based on low-complexity and pipelined architecture by using TSMC 0.18 µm CMOS technology. The operating frequency of the chip can achieve 100 MHz. The core area and the number of logic gates are 598,880 µm2 and 56.3 K, respectively. In addition, this design achieves 50 frames per second, which is suitable for real-time CMOS image sensor compression.

A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph.

Apical Lesions, one of the most common oral diseases, can be effectively detected in daily dental examinations by a periapical radiograph (PA). In the current popular endodontic treatment, most dentists spend a lot of time manually marking the lesion area. In order to reduce the burden on dentists, this paper proposes a convolutional neural network (CNN)-based regional analysis model for spical lesions for periapical radiographs. In this study, the database was provided by dentists with more than three years of practical experience, meeting the criteria for clinical practical application. The contributions of this work are (1) an advanced adaptive threshold preprocessing technique for image segmentation, which can achieve an accuracy rate of more than 96%; (2) a better and more intuitive apical lesions symptom enhancement technique; and (3) a model for apical lesions detection with an accuracy as high as 96.21%. Compared with existing state-of-the-art technology, the proposed model has improved the accuracy by more than 5%. The proposed model has successfully improved the automatic diagnosis of apical lesions. With the help of automation, dentists can focus more on technical and medical diagnoses, such as treatment, tooth cleaning, or medical communication. This proposal has been certified by the Institutional Review Board (IRB) with the certification number 202002030B0.

Operando X-ray Studies of Ni-Containing Heteropolyvanadate Electrode for High-Energy Lithium-Ion Storage Applications.

Ni-containing heteropolyvanadate, Na6[NiV14O40], was synthesized for the first time to be applied in high-energy lithium storage applications as a negative electrode material. Na6[NiV14O40] can be prepared via a facile solution process that is suitable for low-cost mass production. The as-prepared electrode provided a high capacity of approximately 700 mAh g-1 without degradation for 400 cycles, indicating excellent cycling stability. The mechanism of charge storage was investigated using operando X-ray absorption spectroscopy (XAS), X-ray diffraction (XRD), transition X-ray microscopy (TXM), and density functional theory (DFT) calculations. The results showed that V5+ was reduced to V2+ during lithiation, indicating that Na6[NiV14O40] is an insertion-type material. In addition, Na6[NiV14O40] maintained its amorphous structure with negligible volume expansion/contraction during cycling. Employed as the negative electrode in a lithium-ion battery (LIB), the Na6[NiV14O40]//LiFePO4 full cell had a high energy density of 300 W h kg-1. When applied in a lithium-ion capacitor, the Na6[NiV14O40]//expanded mesocarbon microbead full cell displayed energy densities of 218.5 and 47.9 W h kg-1 at power densities of 175.7 and 7774.2 W kg-1, respectively. These findings reveal that the negative electrode material Na6[NiV14O40] is a promising candidate for Li-ion storage applications.

Fast Control for Backlight Power-Saving Algorithm Using Motion Vectors from the Decoded Video Stream.

Backlight power-saving algorithms can reduce the power consumption of the display by adjusting the frame pixels with optimal clipping points under some tradeoff criteria. However, the computation for the selected clipping points can be complex. In this paper, a novel algorithm is created to reduce the computation time of the state-of-the-art backlight power-saving algorithms. If the current frame is similar to the previous frame, it is unnecessary to execute the backlight power-saving algorithm for the optimal clipping points, and the derived clipping point from the previous frame can be used for the current frame automatically. In this paper, the motion vector information was used as the measurement of the similarity between adjacent frames, where the generation of the motion vector information requires no extra complexity since it is generated to reconstruct the decoded frame pixels before the display. The experiments showed that the proposed work can reduce the running time of the state-of-the-art methods by 25.21% to 64.22%, while the performances are maintained; the differences with the state-of-the-art methods in PSNR are only 0.02~1.91 dB, and those in power are only -0.001~0.008 W.

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph.

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion.

Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs.

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early, the treatment will be relatively easy, which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However, the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu's threshold image enhancement technology, this research solves the problem that the original cutting technology cannot extract certain single teeth, and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN), which can identify caries and restorations from the bitewing images. Moreover, it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image, which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization, (2) a dental image cropping procedure to obtain individually separated tooth samples, and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks, namely, AlexNet, GoogleNet, Vgg19, and ResNet50, experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%, respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film.

Polyoxometalate adsorbed in a metal-organic framework for electrocatalytic dopamine oxidation.

A redox-active vanadium-based polyoxometalate, V10O28, was post-synthetically immobilized into a water-stable zirconium-based metal-organic framework, NU-902. The adsorbed V10O28 in NU-902 renders charge hopping in the framework in aqueous electrolytes, and the obtained V10O28@NU-902 can be used as a heterogeneous electrocatalyst for electrochemical dopamine sensors.

Hierarchical Micro/Mesoporous Carbons Synthesized with a ZnO Template and Petroleum Pitch via a Solvent-Free Process for a High-Performance Supercapacitor.

Hierarchical micro/mesoporous carbons were prepared using ZnO nanoparticles as hard templates and a petroleum industrial-residual pitch as the carbon source via a solvent-free process. The ZnO templates can be easily removed using HCl(aq), thereby avoiding limitations present in conventional porous silica templating approaches that require highly corrosive HF(aq) for template removal. Notably, the proposed solvent-free synthetic method from low-cost pitch to high-value porous carbons is a friendly process with respect to our overexploited environment. With the combination of ZnO nanoparticles and pitch, the surface area (76-548 m2 g-1) of the resultant mesoporous carbons increases with an increase in the weight ratios of ZnO to pitch. Furthermore, the hierarchical micro/mesoporous carbons with a large surface area (854-1979 m2 g-1) can be feasibly fabricated by only adding an appropriate amount of an activating agent. Meanwhile, N-doped hierarchical porous carbons can be achieved by carbonizing the blend of these materials with melamine. For supercapacitor application, the resultant carbons exhibit a high capacitance up to 200.5 F g-1 at 5 mV s-1 using LiClO4/PC as the electrolyte in a symmetrical two-electrode cell. More importantly, the coin-cell supercapacitor based on porous carbons achieved a capacitance of 94 F g-1 at 5 mV s-1 and 63% capacitance retention at 500 mV s-1, thereby holding the potential for commercialization.

Microfluidic Chip Coupled with Thermal Desorption Atmospheric Pressure Ionization Mass Spectrometry.

Microfluidic chips have been used as platforms for a diversity of research purposes such as for separation and micro-reaction. One of the suitable detectors for microfluidic chip is mass spectrometry. Because microfluidic chips are generally operated in an open air condition, mass spectrometry coupled with atmospheric pressure ion sources can suit the requirement with minimum compromise. In this study, we develop a new interface to couple a microfluidic chip with mass spectrometry. A capillary tip coated with a layer of graphite, capable of absorbing energy of near-infrared (NIR) light is used to interface microfluidic chip with mass spectrometry. An NIR laser diode (λ=808 nm) is used to irradiate the capillary tip for assisting the generation of spray from the eluent of the microfluidic chip. An electrospray is provided to fuse with the spray generated from the microfluidic chip for post-ionization. Transesterification is used as the example to demonstrate the feasibility of using this interface to couple microfluidic chip with mass spectrometry.