Injectable 2D Material-Based Sensor Array for Minimally Invasive Neural Implants.

Intracranial implants for diagnosis and treatment of brain diseases have been developed over the past few decades. However, the platform of conventional implantable devices still relies on invasive probes and bulky sensors in conjunction with large-area craniotomy and provides only limited biometric information. Here, we report an implantable multi-modal sensor array that can be injected through a small hole in the skull and inherently spread out for conformal contact with the cortical surface. The injectable sensor array, composed of graphene multi-channel electrodes for neural recording and electrical stimulation and MoS2-based sensors for monitoring intracranial temperature and pressure, was designed based on a mesh structure whose elastic restoring force enables the contracted device to spread out. We demonstrated that the sensor array injected into a rabbit's head can detect epileptic discharges on the surface of the cortex and mitigate it by electrical stimulation while monitoring both intracranial temperature and pressure. This method provides good potential for implanting a variety of functional devices via minimally invasive surgery. This article is protected by copyright. All rights reserved.

High energy density in artificial heterostructures through relaxation time modulation.

Electrostatic capacitors are foundational components of advanced electronics and high-power electrical systems owing to their ultrafast charging-discharging capability. Ferroelectric materials offer high maximum polarization, but high remnant polarization has hindered their effective deployment in energy storage applications. Previous methodologies have encountered problems because of the deteriorated crystallinity of the ferroelectric materials. We introduce an approach to control the relaxation time using two-dimensional (2D) materials while minimizing energy loss by using 2D/3D/2D heterostructures and preserving the crystallinity of ferroelectric 3D materials. Using this approach, we were able to achieve an energy density of 191.7 joules per cubic centimeter with an efficiency greater than 90%. This precise control over relaxation time holds promise for a wide array of applications and has the potential to accelerate the development of highly efficient energy storage systems.

A comprehensive analysis of the emerging modern trends in research on photovoltaic systems and desalination in the era of artificial intelligence and machine learning.

Integration of photovoltaic (PV) systems, desalination technologies, and Artificial Intelligence (AI) combined with Machine Learning (ML) has introduced a new era of remarkable research and innovation. This review article thoroughly examines the recent advancements in the field, focusing on the interplay between PV systems and water desalination within the framework of AI and ML applications, along with it analyses current research to identify significant patterns, obstacles, and prospects in this interdisciplinary field. Furthermore, review examines the incorporation of AI and ML methods in improving the performance of PV systems. This includes raising their efficiency, implementing predictive maintenance strategies, and enabling real-time monitoring. It also explores the transformative influence of intelligent algorithms on desalination techniques, specifically addressing concerns pertaining to energy usage, scalability, and environmental sustainability. This article provides a thorough analysis of the current literature, identifying areas where research is lacking and suggesting potential future avenues for investigation. These advancements have resulted in increased efficiency, decreased expenses, and improved sustainability of PV system. By utilizing artificial intelligence technologies, freshwater productivity can increase by 10 % and efficiency. This review offers significant and informative perspectives for researchers, engineers, and policymakers involved in renewable energy and water technology. It sheds light on the latest advancements in photovoltaic systems and desalination, which are facilitated by AI and ML. The review aims to guide towards a more sustainable and technologically advanced future.

Nonconventional Strain Engineering for Uniform Biaxial Tensile Strain in MoS2 Thin Film Transistors.

Strain engineering has been employed as a crucial technique to enhance the electrical properties of semiconductors, especially in Si transistor technologies. Recent theoretical investigations have suggested that strain engineering can also markedly enhance the carrier mobility of two-dimensional (2D) transition-metal dichalcogenides (TMDs). The conventional methods used in strain engineering for Si and other bulk semiconductors are difficult to adapt to ultrathin 2D TMDs. Here, we report a strain engineering approach to apply the biaxial tensile strain to MoS2. Metal-organic chemical vapour deposition (MOCVD)-grown large-area MoS2 films were transferred onto SiO2/Si substrate, followed by the selective removal of the underneath Si. The release of compressive residual stress in the oxide layer induces strain in MoS2 on top of the SiO2 layer. The amount of strain can be precisely controlled by the thickness of oxide stressors. After the transistors were fabricated with strained MoS2 films, the array of strained transistors was transferred onto plastic substrates. This process ensured that the MoS2 channels maintained a consistent tensile strain value across a large area.

Multiple testing of composite null hypotheses for discrete data using randomized p-values.

P-values that are derived from continuously distributed test statistics are typically uniformly distributed on (0,1) under least favorable parameter configurations (LFCs) in the null hypothesis. Conservativeness of a p-value P (meaning that P is under the null hypothesis stochastically larger than uniform on (0,1)) can occur if the test statistic from which P is derived is discrete, or if the true parameter value under the null is not an LFC. To deal with both of these sources of conservativeness, we present two approaches utilizing randomized p-values. We illustrate their effectiveness for testing a composite null hypothesis under a binomial model. We also give an example of how the proposed p-values can be used to test a composite null in group testing designs. We find that the proposed randomized p-values are less conservative compared to nonrandomized p-values under the null hypothesis, but that they are stochastically not smaller under the alternative. The problem of establishing the validity of randomized p-values has received attention in previous literature. We show that our proposed randomized p-values are valid under various discrete statistical models, which are such that the distribution of the corresponding test statistic belongs to an exponential family. The behavior of the power function for the tests based on the proposed randomized p-values as a function of the sample size is also investigated. Simulations and a real data example are used to compare the different considered p-values.

2D Materials in Flexible Electronics: Recent Advances and Future Prospectives.

Flexible electronics have recently gained considerable attention due to their potential to provide new and innovative solutions to a wide range of challenges in various electronic fields. These electronics require specific material properties and performance because they need to be integrated into a variety of surfaces or folded and rolled for newly formatted electronics. Two-dimensional (2D) materials have emerged as promising candidates for flexible electronics due to their unique mechanical, electrical, and optical properties, as well as their compatibility with other materials, enabling the creation of various flexible electronic devices. This article provides a comprehensive review of the progress made in developing flexible electronic devices using 2D materials. In addition, it highlights the key aspects of materials, scalable material production, and device fabrication processes for flexible applications, along with important examples of demonstrations that achieved breakthroughs in various flexible and wearable electronic applications. Finally, we discuss the opportunities, current challenges, potential solutions, and future investigative directions about this field.

Perovskite Light-Emitting Diode Display Based on MoS2 Backplane Thin-Film Transistors.

The uniform deposition of perovskite light-emitting diodes (PeLEDs) and their integration with backplane thin-film transistors (TFTs) remain challenging for large-area display applications. Herein, an active-matrix PeLED display fabricated via the heterogeneous integration of cesium lead bromide LEDs and molybdenum disulfide (MoS2 )-based TFTs is presented. The single-source evaporation method enables the deposition of highly uniform perovskite thin films over large areas. PeLEDs are integrated with MoS2 TFTs to fabricate an active-matrix PeLED display with an 8 × 8 array, which exhibits excellent brightness control capability and high switching speed. This study demonstrates the potential of PeLEDs as candidates for next-generation displays and presents a novel approach for fabricating optoelectronic devices via the heterogeneous integration of 2D materials and perovskites, thereby paving the way toward the fabrication of practical future optoelectronic systems.

A comprehensive study of artificial neural network for sensitivity analysis and hazardous elements sorption predictions via bone char for wastewater treatment.

Using bone char for contaminated wastewater treatment and soil remediation is an intriguing approach to environmental management and an environmentally friendly way of recycling waste. The bone char remediation strategy for heavy metal-polluted wastewater was primarily affected by bone char characteristics, factors of solution, and heavy metal (HM) chemistry. Therefore, the optimal parameters of HM sorption by bone char depend on the research being performed. Regarding enhancing HM immobilization by bone char, a generic strategy for determining optimal parameters and predicting outcomes is crucial. The primary objective of this research was to employ artificial neural network (ANN) technology to determine the optimal parameters via sensitivity analysis and to predict objective function through simulation. Sensitivity analysis found that for multi-metals sorption (Cd, Ni, and Zn), the order of significance for pyrolysis parameters was reaction temperature > heating rate > residence time. The primary variables for single metal sorption were solution pH, HM concentration, and pyrolysis temperature. Regarding binary sorption, the incubation parameters were evaluated in the following order: HM concentrations > solution pH > bone char mass > incubation duration. This approach can be used for further experiment design and improve the immobilization of HM by bone char for water remediation.

Monolithic 3D integration of 2D materials-based electronics towards ultimate edge computing solutions.

Three-dimensional (3D) hetero-integration technology is poised to revolutionize the field of electronics by stacking functional layers vertically, thereby creating novel 3D circuity architectures with high integration density and unparalleled multifunctionality. However, the conventional 3D integration technique involves complex wafer processing and intricate interlayer wiring. Here we demonstrate monolithic 3D integration of two-dimensional, material-based artificial intelligence (AI)-processing hardware with ultimate integrability and multifunctionality. A total of six layers of transistor and memristor arrays were vertically integrated into a 3D nanosystem to perform AI tasks, by peeling and stacking of AI processing layers made from bottom-up synthesized two-dimensional materials. This fully monolithic-3D-integrated AI system substantially reduces processing time, voltage drops, latency and footprint due to its densely packed AI processing layers with dense interlayer connectivity. The successful demonstration of this monolithic-3D-integrated AI system will not only provide a material-level solution for hetero-integration of electronics, but also pave the way for unprecedented multifunctional computing hardware with ultimate parallelism.

Seasonal and Temporal Trends of Infectious Keratitis in Northern Vietnam.

The epidemiology of corneal ulcers in Vietnam has not been well characterized. In this report, we reviewed retrospectively the microbiological data of patients with a clinical diagnosis of corneal ulcer at the microbiology laboratory of Vietnam National Eye Hospital from January 1, 2010 to March 31, 2023. We observed a seasonal pattern for fungal and microsporidial keratitis, with an annual peak in November, and an inverse relationship between fungal keratitis and inclement weather. The November peak coincided with one of the major harvesting seasons in Vietnam. We also observed increasing numbers of microsporidial and Acanthamoeba keratitis cases in recent years. Knowledge of these trends are helpful in guiding empirical treatment of corneal infections and preventing corneal blindness.

Reduced Defect Density in MOCVD-Grown MoS2 by Manipulating the Precursor Phase.

Advancements in the synthesis of large-area, high-quality two-dimensional transition metal dichalcogenides such as MoS2 play a crucial role in the development of future electronic and optoelectronic devices. The presence of defects formed by sulfur vacancies in MoS2 results in low photoluminescence emission and imparts high n-type doping behavior, thus substantially affecting material quality. Herein, we report a new method in which single-phase (liquid) precursors are used for the metal-organic chemical vapor deposition (MOCVD) growth of a MoS2 film. Furthermore, we fabricated a high-performance photodetector (PD) and achieved improved photoresponsivity and faster photoresponse in the spectral range 405-637 nm compared to those of PDs fabricated by the conventional MOCVD method. In addition, the fabricated MoS2 thin film showed a threshold voltage shift in the positive gate bias direction owing to the reduced number of S vacancy defects in the MoS2 lattice. Thus, our method significantly improved the synthesis of monolayer MoS2 and can expand the application scope of high-quality, atomically thin materials in large-scale electronic and optoelectronic devices.

High efficient COVID-19 waste co-pyrolysis char/TiO2 nanocomposite for photocatalytic reduction of Cr(VI) under visible light.

Titanium dioxide (Titania) nanoparticle-coated biochar derived through co-pyrolysis of COVID-19 waste face mask (WFM) and Moringa oleifera seed cake (MO) provides an effective way to alleviate toxic metal in wastewater. This study investigates the effects of Biochar/titania photocatalyst preparation, characterization, and its photoreduction of Cr(VI). The morphological and functional modifications in the catalyst were identified using X-Ray diffraction, scanning electron microscopy, Fourier transform infrared spectroscopy, ultraviolet spectrophotometer, surface area analysis, and Raman spectrophotometer, respectively. The influencing parameters, namely, pH, photocatalyst dosage, initial pollutant concentration, and visible light irradiation time, have been investigated. The findings reveal that the Cr(VI) reduction by the photocatalyst was highly facilitated by photocatalytic process. The prepared photocatalyst shows higher and faster reduction rate of Cr(VI) and also improves the catalyst stability. The photoreduction of Cr(VI) ensembles well with pseudo-first order kinetics. At 180 min of reaction time, maximum Cr(VI) reduction of 98.65% was achieved at pH 2, 0.3 g/L catalyst dosage, and 10 ppm initial concentration, respectively. The synthesized photocatalyst shows excellent recycling performance up to 7 times, and these studies proved that the prepared catalyst is cost-effective and efficiently employed for removing pollutants.

Tetracycline-adsorbed biochar for solid biofuel usage to achieve waste utilization for environmental sustainability.

Biochar is widely used to remove organic pollutants from the environment. Several studies have focused on pollutant removal via biochar adsorption. However, research on the subsequent processing of pollutant-adsorbed biochar is lacking. This study explored the potential of biochar for the adsorption of an aquatic organic pollutant (tetracycline) and its subsequent use as a solid biofuel. These results suggest that corn straw-derived biochar (torrefaction and pyrolysis) is suitable for two-stage utilization to achieve bioresource valorization for environmental sustainability. Tetracycline-adsorbed biochar, particularly biochar pyrolyzed at 600 °C, is suitable for use as a biofuel. The biochar produced via torrefaction (300 °C) and pyrolysis (600 °C) is the optimal choice, with surface area, contact angle, graphitization degree, calorific value, enhancement factor, and upgrading energy index values of 172.48 m2/g, 120.4°, 3.87, 26.983 MJ/kg, 1.58, and 33.72, respectively. This is supported by the results of expense calculation, comprehensive performance analysis, and life-cycle assessment. Overall, the biochar produced in this study is suitable for organic pollutant removal and as solid biofuel; thus, it can be used to realize waste utilization for environmental sustainability.

Low-temperature growth of MoS2 on polymer and thin glass substrates for flexible electronics.

Recent advances in two-dimensional semiconductors, particularly molybdenum disulfide (MoS2), have enabled the fabrication of flexible electronic devices with outstanding mechanical flexibility. Previous approaches typically involved the synthesis of MoS2 on a rigid substrate at a high temperature followed by the transfer to a flexible substrate onto which the device is fabricated. A recurring drawback with this methodology is the fact that flexible substrates have a lower melting temperature than the MoS2 growth process, and that the transfer process degrades the electronic properties of MoS2. Here we report a strategy for directly synthesizing high-quality and high-crystallinity MoS2 monolayers on polymers and ultrathin glass substrates (thickness ~30 µm) at ~150 °C using metal-organic chemical vapour deposition. By avoiding the transfer process, the MoS2 quality is preserved. On flexible field-effect transistors, we achieve a mobility of 9.1 cm2 V-1 s-1 and a positive threshold voltage of +5 V, which is essential for reducing device power consumption. Moreover, under bending conditions, our logic circuits exhibit stable operation while phototransistors can detect light over a wide range of wavelengths from 405 nm to 904 nm.

Investigation of thermodynamic and kinetic parameters of Albizia lebbeck seed pods using thermogravimetric analysis.

Thermodynamic and kinetic studies are very necessary to evaluate the conversion efficiency of biomass to energy. Therefore, this current work reported the thermodynamic and kinetic parameters of Albizia lebbeck seed pods through thermogravimetric analysis, which was carried out at temperatures from 25 °C to 700 °C, and heating rates of 5, 10, 15, and 20 °C/min. Apparent activation energies were determined by applying three iso-conversional model-free methods including Kissinger-Akahira-Sunose (KAS), Ozawa-Flynn-Wall (OFW), and Starink. Resultantly, average apparent activation energy values for the three models of KAS, OFW, and Starink were found to be 155.29, 156.14, and 155.53 kJ/mol, respectively. In addition, thermodynamic triplets such as enthalpy, Gibbs free energy, and entropy were obtained as 151.16 kJ/mol, 150.64 kJ/mol, and -7.57 J/mol·K, respectively. The above results suggest Albizia lebbeck seed pods could become a potential source for bioenergy production aiming to achieve the sustainable goal and waste-to-energy strategy.

Steam explosion as sustainable biomass pretreatment technique for biofuel production: Characteristics and challenges.

The biorefining process of lignocellulosic biomass has recently emerged as one of the most profitable biofuel production options. However, pretreatment is required to improve the recalcitrant lignocellulose's enzymatic conversion efficiency. Among biomass pretreatment methods, the steam explosion is an eco-friendly, inexpensive, and effective approach to pretreating biomass, significantly promoting biofuel production efficiency and yield. This review paper critically presents the steam explosion's reaction mechanism and technological characteristics for lignocellulosic biomass pretreatment. Indeed, the principles of steam explosion technology for lignocellulosic biomass pretreatment were scrutinized. Moreover, the impacts of process factors on pretreatment efficiency and sugar recovery for the following biofuel production were also discussed in detail. Finally, the limitations and prospects of steam explosion pretreatment were mentioned. Generally, steam explosion technology applications could bring great potential in pretreating biomass, although deeper studies are needed to deploy this method on industrial scales.

A review of biowaste remediation and valorization for environmental sustainability: Artificial intelligence approach.

Biowaste remediation and valorization for environmental sustainability focuses on prevention rather than cleanup of waste generation by applying the fundamental recovery concept through biowaste-to-bioenergy conversion systems - an appropriate approach in a circular bioeconomy. Biomass waste (biowaste) is discarded organic materials made of biomass (e.g., agriculture waste and algal residue). Biowaste is widely studied as one of the potential feedstocks in the biowaste valorization process due to its being abundantly available. In terms of practical implementations, feedstock variability from biowaste, conversion costs and supply chain stability prevent the widespread usage of bioenergy products. Biowaste remediation and valorization have used artificial intelligence (AI), a newly developed idea, to overcome these difficulties. This report analyzed 118 works that applied various AI algorithms to biowaste remediation and valorization-related research published between 2007 and 2022. Four common AI types are utilized in biowaste remediation and valorization: neural networks, Bayesian networks, decision tree, and multivariate regression. The neural network is the most frequent AI for prediction models, the Bayesian network is utilized for probabilistic graphical models, and the decision tree is trusted for providing tools to assist decision-making. Meanwhile, multivariate regression is employed to identify the relationship between experimental variables. AI is a remarkably effective tool in predicting data, which is reportedly better than the conventional approach owing to its characteristics of time-saving and high accuracy. The challenge and future work in biowaste remediation and valorization are briefly discussed to maximize the model's performance.

Intelligent approaches for sustainable management and valorisation of food waste.

Food waste (FW) is a severe environmental and social concern that today's civilization is facing. Therefore, it is necessary to have an efficient and sustainable solution for managing FW bioprocessing. Emerging technologies like the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) are critical to achieving this, in which IoT sensors' data is analyzed using AI and ML techniques, enabling real-time decision-making and process optimization. This work describes recent developments in valorizing FW using novel tactics such as the IoT, AI, and ML. It could be concluded that combining IoT, AI, and ML approaches could enhance bioprocess monitoring and management for generating value-added products and chemicals from FW, contributing to improving environmental sustainability and food security. Generally, a comprehensive strategy of applying intelligent techniques in conjunction with government backing can minimize FW and maximize the role of FW in the circular economy toward a more sustainable future.

Microalgae bio-oil production by pyrolysis and hydrothermal liquefaction: Mechanism and characteristics.

Microalgae have great potential in producing energy-dense and valuable products via thermochemical processes. Therefore, producing alternative bio-oil to fossil fuel from microalgae has rapidly gained popularity due to its environmentally friendly process and elevated productivity. This current work aims to review comprehensively the microalgae bio-oil production using pyrolysis and hydrothermal liquefaction. In addition, core mechanisms of pyrolysis and hydrothermal liquefaction process for microalgae were scrutinized, showing that the presence of lipids and proteins could contribute to forming a large amount of compounds containing O and N elements in bio-oil. However, applying proper catalysts and advanced technologies for the two aforementioned approaches could improve the quality, heating value, and yield of microalgae bio-oil. In general, microalgae bio-oil produced under optimal conditions could have 46 MJ/kg heating value and 60% yield, indicating that microalgae bio-oil could become a promising alternative fuel for transportation and power generation.

Thermochemical conversion of large-size woody biomass for carbon neutrality: Principles, applications, and issues.

Large-size woody biomass is a valuable renewable resource to replace fossil fuels in biorefinery processes. The preprocessing of wood chips and briquettes is challenging to manage, especially in an industrial setting, as it generates a significant amount of dust and noise and occasionally causes unexpected accidents. As a result, a substantial amount of resources, energy, labor, and space are needed. The thermochemical conversion behavior of large-size woody biomass was studied to reduce energy consumption for chipping. Large-size wood was 1.5 m in length, 0.1 m in breadth, and stacked 90 cm in height. This strategy has many benefits, including increased effectiveness and reduced CO2 emissions. The target of this paper presents the thermochemical process, and large-size wood was chosen because it provides high-quality product gas while reducing the preprocessing fuel cost. This review examines the benefits of thermochemical conversion technologies for assessing the likelihood of carbon neutrality.