Current Technology Research Articles

Reviving spent lithium‐ion batteries: The advancements and challenges of sustainable black mass recovery

AbstractIdeally, once batteries reach their end‐of‐life, they are expected to be collected, dismantled, and converted into black mass (BM), which contains significant amounts of valuable metals. BM can be regarded as a sort of urban mine, where recyclers extract and reintroduce the materials into new battery manufacturing. Focusing on BM, this article discusses the necessity of BM recovery and current recycling situations. Although the benefits of recycling are widely acknowledged, many challenges and issues remain. The BM market is still in its infancy and relevant regulatory frameworks need to be updated with respect to the widespread use and advancement of lithium‐ion batteries. Current BM producing and processing technologies are gaining momentum and still have room for large improvements in terms of economic feasibility and environmental footprint. Finding solutions for these challenges in the end requires efforts from both researchers and industrial stakeholders with growing interests and long‐term patient engagement. Battery regulations and legal support are highly anticipated for industries to keep high levels of commitment to long‐term investments.

Thermal Plasma Spheroidization and Characterization of Stainless Steel Powders Using Direct Current Plasma Technology

The production of spherical powders has recently registered a boost due to the need to fabricate new printing materials for Additive Manufacturing applications, from polymers and resins to metals and ceramics. Among these materials, stainless steels powders play a leading role, since they are widely used in industry and everyday life; indeed, micron-sized spherical stainless steel powders have specific characteristics and are considered as one of the best candidates for Additive Manufacturing systems and for application in a wide range of sectors. In this paper, stainless steel 316 L powders were used to explore and identify the best process parameters of a thermal plasma process able to produce spherical powders for Additive Manufacturing applications. X-ray Diffraction, Scanning Electron Microscopy, Particle Size Distribution and Flowability analysis were performed to characterize reagents and products. Powders with a high circularity (>0.8) and improved flowability (<30 s/50 g) were successfully obtained. The collected results were compared with data available from the literature to identify the potential use of the spherical produced powders.

LabelSeg: segment annotation for tumor copy number alteration profiles.

Somatic copy number alterations (SCNAs) are a predominant type of oncogenomic alterations that affect a large proportion of the genome in the majority of cancer samples. Current technologies allow high-throughput measurement of such copy number aberrations, generating results consisting of frequently large sets of SCNA segments. However, the automated annotation and integration of such data are particularly challenging because the measured signals reflect biased, relative copy number ratios. In this study, we introduce labelSeg, an algorithm designed for rapid and accurate annotation of CNA segments, with the aim of enhancing the interpretation of tumor SCNA profiles. Leveraging density-based clustering and exploiting the length-amplitude relationships of SCNA, our algorithm proficiently identifies distinct relative copy number states from individual segment profiles. Its compatibility with most CNA measurement platforms makes it suitable for large-scale integrative data analysis. We confirmed its performance on both simulated and sample-derived data from The Cancer Genome Atlas reference dataset, and we demonstrated its utility in integrating heterogeneous segment profiles from different data sources and measurement platforms. Our comparative and integrative analysis revealed common SCNA patterns in cancer and protein-coding genes with a strong correlation between SCNA and messenger RNA expression, promoting the investigation into the role of SCNA in cancer development.

Electrostatic chuck having compliant multi-beam structures with rotatable bipolar pad-shaped electrodes

The demand for the manipulation of flexible film-like objects and those with non-planar surfaces, which cannot be handled using current technologies, has increased considerably. A bipolar electrostatic chuck (ESC) is a device that clamps target objects using a uniform electrostatic force. Traditional ESCs cannot satisfy these demands because of their planar surfaces. Bipolar ESCs with a compliant multi-beam assembly have been proposed to manipulate such objects while leveraging the uniform force distribution of ESCs. Although the proposed compliant ESCs have the potential for such manipulations, increasing the attractive force remains a critical technical issue. This study aims to develop a compliant ESC with pad-shaped bipolar electrodes connected to beam tips with rotational degrees of freedom (RDOFs). Four ESC models with pad-shaped electrodes with different RDOFs between zero and three and one without a pad-shaped electrode are fabricated via 3D printing. The experimental comparison of the five models indicates that an increase in the RDOFs significantly improves the attractive force of each electrode. The high potential of compliant ESCs with three RDOFs at the beam tips is illustrated using three different demonstrations: picking and placing dielectric films, a rigid object with a plane surface, and a rigid object with a curved surface. The results provide insights into the development of industrial electrostatic grippers.

Kirigami electronics for long-term electrophysiological recording of human neural organoids and assembloids.

Realizing the full potential of organoids and assembloids to model neural development and disease will require improved methods for long-term, minimally invasive recording of electrical activity. Current technologies, such as patch clamp, penetrating microelectrodes, planar electrode arrays and substrate-attached flexible electrodes, do not allow chronic recording of organoids in suspension, which is necessary to preserve architecture. Inspired by kirigami art, we developed flexible electronics that transition from a two-dimensional to a three-dimensional basket-like configuration with either spiral or honeycomb patterns to accommodate the long-term culture of organoids in suspension. Here we show that this platform, named kirigami electronics (KiriE), integrates with and enables chronic recording of cortical organoids for up to 120 days while preserving their morphology, cytoarchitecture and cell composition. We demonstrate integration of KiriE with optogenetic and pharmacological manipulation and modeling phenotypes related to a genetic disease. Moreover, KiriE can capture corticostriatal connectivity in assembloids following optogenetic stimulation. Thus, KiriE will enable investigation of disease and activity patterns underlying nervous system assembly.

Probabilistic Pretactical Arrival and Departure Flight Delay Prediction with Quantile Regression

Airports plan their resources well in advance based on anticipated traffic. Currently, the only traffic information accessible in the pretactical phase is the flight schedules and historical data. In practice, however, flights do not always depart or arrive on time for a variety of reasons, such as air traffic flow management or reactionary delays. Because neither air traffic flow management regulations nor aircraft rotations are known during the pretactical phase, predicting the precise arrival and departure delays of individual flights is challenging given current technologies. As a result, probabilistic flight delay predictions are more plausible. This paper presents a machine learning model trained on historical data that learned the various quantiles of the departure and arrival delay distributions of individual flights. The model makes use of input features available during the pretactical phase, such as the airline, aircraft type, or expected number of passengers, to provide predictions of the delay distribution several days before operations. The performance of the model trained on operational data from Geneva airport is compared to a statistical baseline, providing evidence that machine learning is superior. Furthermore, the contribution of the various input features is quantified using the Shapely method, stressing the importance of the expected number of passengers. Finally, some practical examples are presented to illustrate how such a model could be applied in the pretactical phase.

Ultra-compact electro-optic phase modulator based on a lithium niobate topological slow light waveguide.

Electro-optic modulators (EOMs) are essential devices of optical communications and quantum computing systems. In particular, ultra-compact EOMs are necessary for highly integrated photonic chips. Thin film lithium niobate materials are a promising platform for designing highly efficient EOMs. However, EOMs based on conventional waveguide structures are at a millimeter scale and challenging to scale down further, greatly hindering the capability of on-chip integration. Here, we design an EOM based on lithium niobate valley photonic crystal (VPC) structures for the first time. Due to the high effective refractive index introduced by the strong slow light effect, the EOM can achieve an ultra-compact size of 4 μm×14 μm with a half-wave voltage of 1.4 V. The EOM has a high transmittance of 0.87 in the 1068 nm because of the unique spin-valley locking effect in VPC structures. The design is fully compatible with current nanofabrication technology and immune to fabrication defects. Therefore, it opens a new possibility in designing lithium niobate electro-optic modulators and will find broad applications in optical communication and quantum photonic devices.

A Survey of Recent Advances in Deep Learning Models for Detecting Malware in Desktop and Mobile Platforms

Malware is one of the most common and severe cyber threats today. Malware infects millions of devices and can perform several malicious activities including compromising sensitive data, encrypting data, crippling system performance, and many more. Hence, malware detection is crucial to protect our computers and mobile devices from malware attacks. Recently, Deep Learning (DL) has emerged as one of the promising technologies for detecting malware. The recent high production of malware variants against desktop and mobile platforms makes DL algorithms powerful approaches for building scalable and advanced malware detection models as they can handle big datasets. This work explores current deep learning technologies for detecting malware attacks on Windows, Linux, and Android platforms. Specifically, we present different categories of DL algorithms, network optimizers, and regularization methods. Different loss functions, activation functions, and frameworks for implementing DL models are discussed. We also present feature extraction approaches and a review of DL-based models for detecting malware attacks on the above platforms. Furthermore, this work presents major research issues on DL-based malware detection including future research directions to further advance knowledge and research in this field.

Using 3D-VGG-16 and 3D-Resnet-18 deep learning models and FABEMD techniques in the detection of malware

Currently, the detection of malware to prevent cybersecurity breaches is a raising a concern for millions of people around the globe. Even with the most recent updates, antivirus software can leave computer users vulnerable to malware threats and attacks due to the rapid release of new malware. As a result, conventional antivirus software is frequently incapable of identifying novel forms of malware until their unique signatures have been added to the software's database. To protect systems, there is necessity in the current technology field to develop innovative techniques for detecting unidentified malware, particularly during the initial phases of malware deployment. The objective is to address the issue of imbalanced and inadequate malware dataset through the utilization of the fast and adaptive bidirectional empirical mode decomposition (FABEMD) technique. While exploring the Malimg and MaleVis datasets, promising results were reached in reference to accuracy, precision, recall, and F1-score. The experiment demonstrated the accuracy of two distinct 3D architectures, namely 3D VGG-16 and 3D Resnet-18, on two separate datasets: Malimg dataset and MaleVis dataset. The accuracy achieved for VGG-16 was 96.14% and 98.60% for the Malimg and the MaleVis dataset, respectively. In regards to the Resnet-18 architecture, it demonstrated high accuracy rates of 99.64% and 99.46% for the Malimg and MaleVis datasets, respectively.

Emerging Nanomaterials toward Uranium Extraction from Seawater: Recent Advances and Perspectives.

Nuclear energy holds great potential to facilitate the global energy transition and alleviate the increasing environmental issues due to its high energy density, stable energy output, and carbon-free emission merits. Despite being limited by the insufficient terrestrial uranium reserves, uranium extraction from seawater (UES) can offset the gap. However, the low uranium concentration, the complicated uranium speciation, the competitive metal ions, and the inevitable marine interference remarkably affect the kinetics, capacity, selectivity, and sustainability of UES materials. To date, massive efforts have been made with varying degrees of success to pursue a desirable UES performance on various nanomaterials. Nevertheless, comprehensive and systematic coverage and discussion on the emerging UES materials presenting the fast-growing progress of this field is still lacking. This review thus challenges this position and emphatically focuses on this topic covering the current mainstream UES technologies with the emerging UES materials. Specifically, this review elucidates the causality between the physiochemical properties of UES materials induced by the intellectual design strategies and the UES performances and further dissects the relationships of materials-properties-activities and the corresponding mechanisms in depth. This review is envisaged to inspire innovative ideas and bring technical solutions for developing technically and economically viable UES materials.

Selective pseudocapacitive immobilization of REE elements on carbon based electrodes

Current extraction technologies for rare earth elements at ambient conditions are reagent and energy intensive, giving rise to significant quantities of secondary waste streams. Exploration of the removal of common cations from aqueous streams has been undertaken with carbon materials and have shown promising results. However, those promising materials have not been explored extensively for rare earth element capture from aqueous process streams. In this study, a carbon electrode was investigated for the adsorption and immobilization of REE elements from aqueous solutions. Cyclic voltammetry studies of the carbon electrode displayed a pseudocapacitive behavior where it was verified that cation adsorption is accompanied by an electron transfer process. Preliminary tests showed that a current density of 89.1 mA. g−1, allowed for the pseudocapacitive adsorption (PSA) of Nd3+ cation without the formation of rare earth hydroxides. Hence, the selective PSA of Nd3+ was verified in an electrolyte solution with equimolar concentrations of Nd3+, Mg2+, Li+, Na+ and K+, achieving separation factors of 8.6, 1.1, and 10.9 for Nd3+/Li+, Nd3+/Na+, and Nd3+/Mg2+, respectively. Analysis of the potential-time curves for the various cations suggests that storage of ions in the electrode involved pore-spacings rather than interlayer spacings and was corroborated by XRD analysis. Specific capacitance as high as >640 F.g−1 for Nd3+ was also observed for the carbon electrode, with Faradaic efficiencies (FE) of >20 % for Nd3+ and Mg2+, and >9 % for Na+. Furthermore, adsorption capacity of >125 mg g−1 after 4hrs of electrosorption was observed for Nd3+. In the presence of the Nd3+ electrolyte, the electrode achieved increasing storage of Nd3+ with >94 % retention of Nd3+ with a FE > 20 % over 6 cycles between loading and releases in 1 M KCl solution. These preliminary results provide information and some guidance on the selective recovery of rare earth elements, such as Nd, in aqueous streams in the presence of competitive cations employing the use of carbon-based materials.

Managing potential environmental and human health risks of lead halide perovskite photovoltaic modules

Perovskite solar cells (PSCs) are emerging photovoltaic (PV) technologies capable of matching power conversion efficiencies (PCEs) of current PV technologies in the market at lower manufacturing costs, making perovskite solar modules (PSMs) cost competitive if manufactured at scale and perform with minimal degradation. PSCs with the highest PCEs, to date, are lead halide perovskites. Lead presents potential environmental and human health risks if PSMs are to be commercialized, as the lead in PSMs are more soluble in water compared to other PV technologies. Therefore, prior to commercialization of PSMs, it is important to highlight, identify, and establish the potential environmental and human health risks of PSMs as well as develop methods for assessing the potential risks. Here, we identify and discuss a variety of international standards, U.S. regulations, and permits applicable to PSM deployment that relate to the potential environmental and human health risks associated with PSMs. The potential risks for lead and other hazardous material exposures to humans and the environment are outlined which include water quality, air quality, human health, wildlife, land use, and soil contamination, followed by examples of how developers of other PV technologies have navigated human health and environmental risks previously. Potential experimentation, methodology, and research efforts are proposed to elucidate and characterize potential lead leaching risks and concerns pertaining to fires, in-field module damage, and sampling and leach testing of PSMs at end of life. Lastly, lower technology readiness level solutions to mitigate lead leaching, currently being explored for PSMs, are discussed. PSMs have the potential to become a cost competitive PV technology for the solar industry and taking steps toward understanding, identifying, and creating solutions to mitigate potential environmental and human health risks will aid in improving their commercial viability.

Degrade the implement intensity of tripartite operation sharing using six-qubit entangled state

Based on classical communication and some local quantum operations, a novel tripartite operation sharing scheme is put forwarded by employing a six-qubit absolutely maximally entangled state as quantum channel. The finally successful probability of this scheme is deterministic and the target quantum operation to be shared is arbitrary. Besides, the optimized entanglement structure ensures that the numbers of necessary quantum operations to accomplish the task is relatively less. Furthermore, analysis shows that the present scheme is feasible with the current experimental technology.

Four-Dimensional Flow Echocardiography: Blood Speckle Tracking in Congenital Heart Disease: How to Apply, How to Interpret, What Is Feasible, and What Is Missing Still.

Blood speckle tracking echocardiography (BSTE) is a new, promising 4D flow ultrafast non-focal plane imaging technique. The aim of the present investigation is to provide a review and update on potentialities and application of BSTE in children with congenital heart disease (CHD) and acquired heart disease. A literature search was performed within the National Library of Medicine using the keywords "echocardiography", "BST", and "children". The search was refined by adding the keywords "ultrafast imaging", "CHD", and "4D flow". Fifteen studies were finally included. Our analysis outlined how BSTE is highly feasible, fast, and easy for visualization of normal/abnormal flow patterns in healthy children and in those with CHD. BSTE allows for visualization and basic 2D measures of normal/abnormal vortices forming the ventricles and in the main vessel. Left ventricular vortex characteristics and aortic flow patterns have been described both in healthy children and in those with CHD. Complex analysis (e.g., energy loss, vorticity, and vector complexity) are also highly feasible with BSTE, but software is currently available only for research. Furthermore, current technology allows for BSTE only in neonates and low-weight children (e.g., <40 kg). In summary, the feasibility and potentialities of BSTE as a complementary diagnostic tool in children have been proved; however, its systemic use is hampered by the lack of (i) accessible tools for complex quantification and for acquisition at all ages/weight, (ii) data on the diagnostic/prognostic significance of BSTE, and (iii) consensus/recommendation papers indicating when and how BSTE should be employed.

Verifiable multi-party quantum secret sharing based on GHZ states

In this paper, we propose a verifiable multi-party secret sharing protocol based on an entanglement swapping theorem of GHZ states. In our protocol, all receivers only need to perform simple GHZ measurements to recover the secret. The quantum-safe system proposed in this paper is feasible with current quantum technologies, and we show that it is secure against both external and internal attacks. In addition, the secret sender can verify whether the secret is correctly recovered.

Current Technologies and Future Perspectives in Immunotherapy towards a Clinical Oncology Approach.

Immunotherapy is now established as a potent therapeutic paradigm engendering antitumor immune response against a wide range of malignancies and other diseases by modulating the immune system either through the stimulation or suppression of immune components such as CD4+ T cells, CD8+ T cells, B cells, monocytes, macrophages, dendritic cells, and natural killer cells. By targeting several immune checkpoint inhibitors or blockers (e.g., PD-1, PD-L1, PD-L2, CTLA-4, LAG3, and TIM-3) expressed on the surface of immune cells, several monoclonal antibodies and polyclonal antibodies have been developed and already translated clinically. In addition, natural killer cell-based, dendritic cell-based, and CAR T cell therapies have been also shown to be promising and effective immunotherapeutic approaches. In particular, CAR T cell therapy has benefited from advancements in CRISPR-Cas9 genome editing technology, allowing the generation of several modified CAR T cells with enhanced antitumor immunity. However, the emerging SARS-CoV-2 infection could hijack a patient's immune system by releasing pro-inflammatory interleukins and cytokines such as IL-1β, IL-2, IL-6, and IL-10, and IFN-γ and TNF-α, respectively, which can further promote neutrophil extravasation and the vasodilation of blood vessels. Despite the significant development of advanced immunotherapeutic technologies, after a certain period of treatment, cancer relapses due to the development of resistance to immunotherapy. Resistance may be primary (where tumor cells do not respond to the treatment), or secondary or acquired immune resistance (where tumor cells develop resistance gradually to ICIs therapy). In this context, this review aims to address the existing immunotherapeutic technologies against cancer and the resistance mechanisms against immunotherapeutic drugs, and explain the impact of COVID-19 on cancer treatment. In addition, we will discuss what will be the future implementation of these strategies against cancer drug resistance. Finally, we will emphasize the practical steps to lay the groundwork for enlightened policy for intervention and resource allocation to care for cancer patients.

Percutaneous nephrolithotomy guided by 5G-powered robot-assisted teleultrasound diagnosis system: first clinical experience with a novel tele-assistance approach (IDEAL stage 1)

BackgroundTo demonstrate the technical feasibility of percutaneous nephrolithotomy (PCNL) guided by 5G-powered robot-assisted teleultrasound diagnosis system (RTDS) in a complex kidney-stone (CKS) cohort and present our preliminary outcomes. PCNL is highly skill-required, which hinders it popularization in primary medical units of remote regions. We designed an innovative tele-assistance approach to make PCNL easy to be operated by inexperienced surgeons.MethodsThis was a prospective proof-of-concept study (IDEAL phase 1) on intraoperative tele-assistance provided by online urological experts via a 5G-powered RTDS. Total 15 CKS patients accepted this technology. Online experts manipulated a simulated probe to assist unskilled local operators by driving a patient-side robot-probe to guide and monitor the steps of access establishment and finding residual stones.ResultsMedian total delay was 177ms despite one-way network-connecting distance > 5,800 km. No perceptible delay of audio-visual communication, driving robot-arm or dynamic ultrasound images was fed back. Successful tele-assistance was obtained in all cases. The first-puncture access-success rate was 78.6% with a one-session SF rate of 71.3% and without complications of grade III-V.ConclusionsThe current technology based on 5G-powered RTDS can provide high-quality intraoperative tele-assistance, which has preliminarily shown satisfactory outcomes and reliable safety. It will break down a personal competence-based barrier to endow PCNL with more popular utilization.Trial registrationThe study was approved by ethics committee of the Xinjiang Kezhou People’s Hospital and ethics committee of the First Affiliated Hospital of Nanjing Medical University and was registered on http://www.chictr.org.cn (ChiCTR2200065849, 16/11/2022).

Extracorporeal Blood Therapeutic Devices for Renal Replacement: A Review of Current Technologies and Future Directions Toward Microscale-based Devices

Extracorporeal Blood Therapeutic Devices for Renal Replacement: A Review of Current Technologies and Future Directions Toward Microscale-based Devices

The Implementation of a Gesture Recognition System with a Millimeter Wave and Thermal Imager.

During the COVID-19 pandemic, the number of cases continued to rise. As a result, there was a growing demand for alternative control methods to traditional buttons or touch screens. However, most current gesture recognition technologies rely on machine vision methods. However, this method can lead to suboptimal recognition results, especially in situations where the camera is operating in low-light conditions or encounters complex backgrounds. This study introduces an innovative gesture recognition system for large movements that uses a combination of millimeter wave radar and a thermal imager, where the multi-color conversion algorithm is used to improve palm recognition on the thermal imager together with deep learning approaches to improve its accuracy. While the user performs gestures, the mmWave radar captures point cloud information, which is then analyzed through neural network model inference. It also integrates thermal imaging and palm recognition to effectively track and monitor hand movements on the screen. The results suggest that this combined method significantly improves accuracy, reaching a rate of over 80%.

Ultrasensitive electrochemical sensor for detection of salivary cortisol in stress conditions.

A natural stress response induces elevated cortisol levels in biological fluids, such as saliva. While current sensor technologies can detect cortisol in real time, their sensitivity and reliability for human subjects have not been assured. This is due to relatively low concentrations of salivary cortisol, which fluctuate throughout the day and vary significantly between individuals. To address these challenges, we present an improved electrochemical biosensor leveraging graphene's exceptional conductivity and physicochemical properties. A1-pyrenebutyric acid N-hydroxysuccinimide ester (PBASE-NHS)-modified commercial graphene foam (GF) electrode is presentedto realize an ultra-sensitive biosensor for cortisol detection directly in human saliva. The biosensor fabrication process entails the attachment of anti-cortisol monoclonal antibodies (mAb-cort) onto a PBASE-NHS/GF electrode through noncovalent immobilization on the vertically stratified graphene foam electrode surface. This unique immobilization strategy preserves graphene's structural integrity and electrical conductivity while facilitating antibody immobilization. The binding of cortisol to immobilized mAb-cort is read out via differential pulse voltammetry using ferri/ferro redox reactions. The immunosensor demonstrates an exceptional dynamic range of 1.0fgmL-1 to 10,000pgmL-1 (R2 = 0.9914) with a detection limit of 0.24fgmL-1 (n = 3) for cortisol. Furthermore, we have established the reliability of cortisol sensors in monitoring human saliva. We have also performed multiple modes of validation, one against the established enzyme-linked immunosorbent assay (ELISA) and a second by a third-party service Salimetric on 16 student volunteers exposed to different stress levels, showing excellent correlation (r = 0.9961). These findings suggest the potential for using mAb-cort/PBASE-NHS/GF-based cortisol electrodes for monitoring salivary cortisol in the general population.