Search Results (18)

All-solid-state lithium-ion batteries (ASSLBs) are attractive energy storage devices because of their excellent gravimetric and volumetric capacity and ability to supply high power rates. Porous silicon (Si) is a promising material for an anode in lithium-ion batteries due to its high capacity and low discharge potential. However, Si anodes cause significant problems due to strong volume growth during the lithiation and delithiation processes, which results in rapid capacity fading and poor cycle stability. To overcome this problem, we developed mesoporous silica (SiO₂) aerogels into porous silicon (Si) anodes using a magnesiothermic reduction (MTR) process. By effectively preserving the porous structure, this approach enables the material to endure volume fluctuations while maintaining its structural integrity during cycling. In our study, we demonstrated a feasible approach to fabricate the porous silicon (Si) from hydrophobic and hydrophilic silica (SiO₂) aerogel and magnesium powder (Mg) through the MTR process at 600~900 °C. The sample obtained after the reduction process was treated with hydrochloric acid (HCl) to remove byproducts. As prepared, Si was characterized using various techniques, including XRD, XRF, FT-IR, XPS, SEM, and BET, which confirmed the successful production, chemical purity, and structural retention of Si. Furthermore, the coin cell was fabricated using Si as an anode, and the electrochemical performance was analyzed. The charge/discharge cycling tests at 1 C and 0.02~2 V (vs. the Li condition) revealed the effects of silicon content, wettability, and interfacial compatibility on electrode performance. Conversely, for better understanding, a long-term cycling test was conducted at 1 C rate, 0–1.5 V (vs. Li) to evaluate capacity retention. Our findings highlight the potential application of silicon (Si) aerogels produced from silica (SiO₂) aerogels by magnesiothermic reduction to improve lithium-ion battery performance. Full article

Background: The evolution of “omic” technologies, which measure all biological molecules of a specific type (e.g., genomics), has enabled rapid and cost-effective data acquisition, depending on the technique and sample size. This, however, generates new hurdles that need to be addressed and should be improved upon. This includes selecting the appropriate statistical test based on study design in a high-throughput manner. Methods: An automated statistical analysis pipeline for omic datasets that we coined STATom@ic (pronounced stat-o-matic) was developed in R programming language. Results: We developed an R package that enables statisticians, bioinformaticians, and scientists to perform assumption tests (e.g., normality and variance homogeneity) before selecting appropriate statistical tests. This analysis package can handle two-group and multiple-group comparisons. In addition, this R package can be used for many data formats including normalized counts (RNASeq) and spectral abundance (proteomics and metabolomics). STATom@ic has high precision but lower recall compared to DeSeq2. Conclusions: The STATom@ic R Package is a user-friendly stand-alone or add-on to current bioinformatic workflows that automatically performs appropriate statistical analysis based on the characteristics of the data. Full article

The growing interest in wearable and portable devices has stimulated the need for flexible and stretchable lithium-ion batteries (LiBs). A crucial component in these batteries is the separator, which provides a pathway for Li-ion transfer and prevents electrode contact. In a flexible and stretchable LiB, the separator must exhibit stretchability and elasticity akin to its existing counterparts. Here, we developed a non-modified thermoplastic polyurethane (TPU) separator using the non-solvent induced phase separation (NIPS) technique. We compared their performance with commercially available polypropylene (PP) separators. Our results demonstrate that TPU separators exhibit superior elasticity based on repeated stretch/release tests with excellent thermal stability and electrolyte wettability. Furthermore, our findings confirm that TPU separators, even after being repeatedly stretched and released, can function effectively without severe damage in a fabricated coin cell LiB with high oxidative stability, as evidenced by linear sweep voltammetry, like commercially available separators. Full article

The development of efficient energy storage systems is critical in the transition towards sustainable energy solutions. In this context, the present work investigates the viability of using orange juice, as a promising and sustainable precursor, for the synthesis of activated carbon electrodes for supercapacitor technologies. Through the carbonization-activation process and controlling the preparation parameters (KOH ratio and activation time), we have tailored the specific surface area (SSA) and pore size distribution (PSD) of the resulting carbon materials—crucial parameters that support supercapacitive performance. Several spectroscopic, morphological, and electrochemical techniques are used to characterize the obtained carbon materials. In particular, our optimization efforts revealed that a 5:1 KOH ratio with an activation time up to 120 min produced the highest SSA of about 2203 m²/g. Employing these optimal conditions, we fabricated symmetric coin cell supercapacitors using Na₂SO₄ as the electrolyte, which exhibited interesting specific capacitance (~56 F/g). Durability testing over 5000 cycles sustained the durability of the as-made activated carbon electrodes, suggesting an excellent retention of specific capacitance. This study not only advances the field of energy storage by introducing a renewable material for electrode fabrication but also contributes to the broader goal of waste reduction through the repurposing of food byproducts. Full article

The global hazardous waste management market is expected to reach USD 987.51 million by 2027 at a CAGR of 14.48%. The early detection of corrosive, flammable, and infectious toxicants from natural sources or manmade contaminants from different environments is crucial to ensure the safety and security of the global living system. Even though the emergence of advanced science and technology continuously offers a more comfortable lifestyle, there are two sides of the coin in terms of opportunities and challenges, demanding solutions for greener applications and waste-to-wealth strategies. A modern analytical technique based on an electrochemical approach and microfluidics is one such emerging advanced solution for the early and effective detection of toxicants. This review attempts to highlight the different studies performed in the field of toxicant analysis, especially the fusion of electrochemistry and lab-chip model systems, promising for point-of-need analysis. The contents of this report are organised by classifying the types of toxicants and trends in electrochemical-integrated lab-chip assays that test for heavy-metal ions, food-borne pathogens, pesticides, physiological reactive oxygen/nitrogen species, and microbial metabolites. Future demands in toxicant analysis and possible suggestions in the field of microanalysis-mediated electrochemical (bio)sensing are summarised. Full article

Rapeseed is a significant oil crop, and the size and length of its pods affect its productivity. However, manually counting the number of rapeseed pods and measuring the length, width, and area of the pod takes time and effort, especially when there are hundreds of rapeseed resources to be assessed. This work created two state-of-the-art deep learning-based methods to identify rapeseed pods and related pod attributes, which are then implemented in rapeseed pots to improve the accuracy of the rapeseed yield estimate. One of these methods is YOLO v8, and the other is the two-stage model Mask R-CNN based on the framework Detectron2. The YOLO v8n model and the Mask R-CNN model with a Resnet101 backbone in Detectron2 both achieve precision rates exceeding 90%. The recognition results demonstrated that both models perform well when graphic images of rapeseed pods are segmented. In light of this, we developed a coin-based approach for estimating the size of rapeseed pods and tested it on a test dataset made up of nine different species of Brassica napus and one of Brassica campestris L. The correlation coefficients between manual measurement and machine vision measurement of length and width were calculated using statistical methods. The length regression coefficient of both methods was 0.991, and the width regression coefficient was 0.989. In conclusion, for the first time, we utilized deep learning techniques to identify the characteristics of rapeseed pods while concurrently establishing a dataset for rapeseed pods. Our suggested approaches were successful in segmenting and counting rapeseed pods precisely. Our approach offers breeders an effective strategy for digitally analyzing phenotypes and automating the identification and screening process, not only in rapeseed germplasm resources but also in leguminous plants, like soybeans that possess pods. Full article

In this paper, a hybrid of a Wavelet Decomposition–Generalised Auto-Regressive Conditional Heteroscedasticity–Extreme Value Theory (WD-ARMA-GARCH-EVT) model is applied to estimate the Value at Risk (VaR) of BitCoin (BTC/USD) and the South African Rand (ZAR/USD). The aim is to measure and compare the riskiness of the two currencies. New and improved estimation techniques for VaR have been suggested in the last decade in the aftermath of the global financial crisis of 2008. This paper aims to provide an improved alternative to the already existing statistical tools in estimating a currency VaR empirically. Maximal Overlap Discrete Wavelet Transform (MODWT) and two mother wavelet filters on the returns series are considered in this paper, viz., the Haar and Daubechies (d4). The findings show that BitCoin/USD is riskier than ZAR/USD since it has a higher VaR per unit invested in each currency. At the 99% significance level, BitCoin/USD has average values of VaR of 2.71% and 4.98% for the WD-ARMA-GARCH-GPD and WD-ARMA-GARCH-GEVD models, respectively; and this is slightly higher than the respective 2.69% and 3.59% for the ZAR/USD. The average BitCoin/USD returns of 0.001990 are higher than ZAR/USD returns of −0.000125. These findings are consistent with the mean-variance portfolio theory, which suggests a higher yield for riskier assets. Based on the p-values of the Kupiec likelihood ratio test, the hybrid model adequacy is largely accepted, as p-values are greater than 0.05, except for the WD-ARMA-GARCH-GEVD models at a 99% significance level for both currencies. The findings are helpful to financial risk practitioners and forex traders in formulating their diversification and hedging strategies and ascertaining the risk-adjusted capital requirement to be set aside as a cushion in the event of the occurrence of an actual loss. Full article

Metal coins discovered during archaeology have artistic and cultural value. Careful cleaning is required for artifact conservation. Metal artifacts must be cleaned to remove corrosion, which can range from tarnishing to a thick crust, in addition to dust, previous coatings, and burial deposits. Cleaning corrosion is still a challenging conservation process, but the advantages of using traditional cleaning methods outweigh the disadvantages. The current study aimed to evaluate the use of a nanosecond infrared Q-switched Nd: YAG pulsed laser for biodeteriogen elimination by laser cleaning and elemental analysis via LIBS analysis on old, corroded coins. The corroded coins used in this study were found in Egyptian burial dirt. Four different varieties of unknown corroded coins were exposed to laser cleaning testing. Throughout the cleaning process, LIBS diagnostics was used to monitor the laser ablation process as it removed various types of corrosion products. The coins were analyzed with a scanning electron microscope equipped with an energy-dispersive X-ray analyzer before and after the laser cleaning to assess the efficacy of the suggested laser setup technique used in this experiment (SEM-EDX). The results show a reduction in the spectral lines of corroded metals (Cu, Ca, and Mg) in the investigated coins after cleaning when compared to the original analyses. However, the surface morphology of each coin changes somewhat due to the presence of CuOx, which was recognized by increasing the strength of O lines, ensuring the viability of utilizing LIBS to identify the unknown coins tested. Full article

Micro-expression analysis is the study of subtle and fleeting facial expressions that convey genuine human emotions. Since such expressions cannot be controlled, many believe that it is an excellent way to reveal a human’s inner thoughts. Analyzing micro-expressions manually is a very time-consuming and complicated task, hence many researchers have incorporated deep learning techniques to produce a more efficient analysis system. However, the insufficient amount of micro-expression data has limited the network’s ability to be fully optimized, as overfitting is likely to occur if a deeper network is utilized. In this paper, a complete deep learning-based micro-expression analysis system is introduced that covers the two main components of a general automated system: spotting and recognition, with also an additional element of synthetic data augmentation. For the spotting part, an optimized continuous labeling scheme is introduced to spot the apex frame in a video. Once the apex frames have been recognized, they are passed to the generative adversarial network to produce an additional set of augmented apex frames. Meanwhile, for the recognition part, a novel convolutional neural network, coined as Optimal Compact Network (OC-Net), is introduced for the purpose of emotion recognition. The proposed system achieved the best F1-score of 0.69 in categorizing the emotions with the highest accuracy of 79.14%. In addition, the generated synthetic data used in the training phase also contributed to performance improvement of at least 0.61% for all tested networks. Therefore, the proposed optimized and compact deep learning system is suitable for mobile-based micro-expression analysis to detect the genuine human emotions. Full article

A suitable framework for the development of artificial neural networks is important because it decides the level of accuracy, which can be reached for a certain dataset and increases the certainty about the reached classification results. In this paper, we conduct a comparative study for the performance of four frameworks, Keras with TensorFlow, Pytorch, TensorFlow, and Cognitive Toolkit (CNTK), for the elaboration of neural networks. The number of neurons in the hidden layer of the neural networks is varied from 8 to 64 to understand its effect on the performance metrics of the frameworks. A test dataset is synthesized using an analytical model and real measured impedance spectra by an eddy current sensor coil on EUR 2 and TRY 1 coins. The dataset has been extended by using a novel method based on interpolation technique to create datasets with different difficulty levels to replicate the scenario with a good imitation of EUR 2 coins and to investigate the limit of the prediction accuracy. It was observed that the compared frameworks have high accuracy performance for a lower level of difficulty in the dataset. As the difficulty in the dataset is raised, there was a drop in the accuracy of CNTK and Keras with TensorFlow depending upon the number of neurons in the hidden layers. It was observed that CNTK has the overall worst accuracy performance with an increase in the difficulty level of the datasets. Therefore, the major comparison was confined to Pytorch and TensorFlow. It was observed for Pytorch and TensorFlow with 32 and 64 neurons in hidden layers that there is a minor drop in the accuracy with an increase in the difficulty level of the dataset and was above 90% until both the coins were 80% closer to each other in terms of electrical and magnetic properties. However, Pytorch with 32 neurons in the hidden layer has a reduction in model size by 70% and 16.3% and predicts the class, 73.6% and 15.6% faster in comparison to TensorFlow and Pytorch with 64 neurons. Full article

Surface modified reduced graphene oxide (rGO) aerogels were synthesized using the hydrothermal method. Ethylene diamine (EDA) and α-cyclodextrin (CD) were used to functionalize the surface of the graphene oxide layers. The oxygen reduction and surface modification occurred in-situ during the hydrothermal self-assembly process. The chemical functionality and structure of the resulting ethylene diamine modified (rGO-EDA) and cyclodextrin modified (rGO-CD) aerogels as well as of the pristine unmodified rGO aerogel were studied using XPS, SEM, XRD, and SANS techniques. The overall surface composition showed a significant decrease in the oxygen content for all synthesized aerogels. The surface modified aerogels were characterized by a disordered stacking of the assembled rGO layers. The surface functionalities resulted in a broad distribution of the interlayer spacing and introduced structural heterogeneities. Such disordered structures can enable a better adsorption mechanism of the sodium ions. Coin cells based on the synthesized aerogels and sodium metal were assembled and tested at several charge and discharge rates. The correlation between the surface functionality of the rGO, the induced structural heterogeneities due to the disordered stacking, and the electrochemical performance of sodium-ion batteries were investigated. Operando XRD measurements were carried out during the battery cycling to investigate the adsorption or intercalation nature of the sodiation mechanism. Full article

To improve the accuracy of bracket placement in vivo, a protocol and device were introduced, which consisted of operative procedures for accurate control, a computer-aided design, and an augmented reality–assisted bracket navigation system. The present study evaluated the accuracy of this protocol. Methods: Thirty-one incisor teeth were tested from four participators. The teeth were bonded by novice and expert orthodontists. Compared with the control group by Boone gauge and the experiment group by augmented reality-assisted bracket navigation system, our study used for brackets measurement. To evaluate the accuracy, deviations of positions for bracket placement were measured. Results: The augmented reality-assisted bracket navigation system and control group were used in the same 31 cases. The priority of bonding brackets between control group or experiment group was decided by tossing coins, and then the teeth were debonded and the other technique was used. The medium vertical (incisogingival) position deviation in the control and AR groups by the novice orthodontist was 0.90 ± 0.06 mm and 0.51 ± 0.24 mm, respectively (p < 0.05), and by the expert orthodontist was 0.40 ± 0.29 mm and 0.29 ± 0.08 mm, respectively (p < 0.05). No significant changes in the horizontal position deviation were noted regardless of the orthodontist experience or use of the augmented reality–assisted bracket navigation system. Conclusion: The augmented reality–assisted bracket navigation system increased the accuracy rate by the expert orthodontist in the incisogingival direction and helped the novice orthodontist guide the bracket position within an acceptable clinical error of approximately 0.5 mm. Full article

In this paper, by performing an intertemporal comparison, we investigate two monetary policy regimes where a central bank is absent, and we further refer on the mechanisms they developed so as to ensure the reliability of transactions between the parties involved. In particular, we mainly focus on the economic–monetary institutions of Athens during the Classical period (508–322 BCE) and we argue that (in principle) there are inter-temporal similarities between the Athenian and the current digital currencies regimes regarding the auditing principles with which the reliability of financial transactions is ensured. We found that in both cases, what is crucial for the success of the system is to achieve trust on the currency. By focusing on Classical Athens, we analyze the nature of the mechanisms and the auditing techniques used to ensure reliable commercial transactions. We also briefly analyze the modern cryptocurrency techniques. We found that the success of both financial regimes was based on achieving: low transactional costs, speed in commercial transactions, and what we characterize as security regarding the commercial transactions. Full article

Recently, it has been found that e-commerce (EC) websites provide a large amount of useful information that exceed the human cognitive processing capacity. In order to help customers in comparing alternatives when buying a product, previous research authors have designed opinion summarization systems based on customer reviews. They ignored the template information provided by manufacturers, although its descriptive information has the most useful product characteristics and texts are linguistically correct, unlike reviews. Therefore, this paper proposes a methodology coined as SEOpinion (summarization and exploration of opinions) to summarize aspects and spot opinion(s) regarding them using a combination of template information with customer reviews in two main phases. First, the hierarchical aspect extraction (HAE) phase creates a hierarchy of aspects from the template. Subsequently, the hierarchical aspect-based opinion summarization (HAOS) phase enriches this hierarchy with customers’ opinions to be shown to other potential buyers. To test the feasibility of using deep learning-based BERT techniques with our approach, we created a corpus by gathering information from the top five EC websites for laptops. The experimental results showed that recurrent neural network (RNN) achieved better results (77.4% and 82.6% in terms of F1-measure for the first and second phases, respectively) than the convolutional neural network (CNN) and the support vector machine (SVM) technique. Full article

The failure types and ultimate loads for eight carbon-epoxy laminate specimens with a central circular hole subjected to tensile load were tested experimentally and simulated using two different progressive failure analysis (PFA) methodologies. The first model used a lamina level modeling based on the Hashin criterion and the Camanho stiffness degradation theory to predict the damage of the fiber and matrix. The second model implemented a micromechanical analysis technique coined the generalized method of cells (GMC), where the 3D Tsai–Hill failure criterion was used to govern matrix failure, and the fiber failure was dictated by the maximum stress criterion. The progressive failure methodology was implemented using the UMAT subroutine within the ABAQUS/implicit solver. Results of load versus displacement and failure types from the two different models were compared against experimental data for the open hole laminates subjected to tensile displacement load. The results obtained from the numerical simulation and experiments showed good agreement. Failure paths and accurate damage contours for the tested specimens were also predicted. Full article