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.

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.

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.

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.

The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder.

(1) Background and Goal: Several studies have investigated the association of sleep, diurnal patterns, and circadian rhythms with the presence and with the risk states of mental illnesses such as schizophrenia and bipolar disorder. The goal of our study was to examine actigraphic measures to identify features that can be extracted from them so that a machine learning model can detect premorbid latent liabilities for schizotypy and bipolarity. (2) Methods: Our team developed a small wrist-worn measurement device that collects and identifies actigraphic data based on an accelerometer. The sensors were used by carefully selected healthy participants who were divided into three groups: Control Group (C), Cyclothymia Factor Group (CFG), and Positive Schizotypy Factor Group (PSF). From the data they collected, our team performed data cleaning operations and then used the extracted metrics to generate the feature combinations deemed most effective, along with three machine learning algorithms for categorization. (3) Results: By conducting the training, we were able to identify a set of mildly correlated traits and their order of importance based on the Shapley value that had the greatest impact on the detection of bipolarity and schizotypy according to the logistic regression, Light Gradient Boost, and Random Forest algorithms. (4) Conclusions: These results were successfully compared to the results of other researchers; we had a similar differentiation in features used by others, and successfully developed new ones that might be a good complement for further research. In the future, identifying these traits may help us identify people at risk from mental disorders early in a cost-effective, automated way.

Vertical Conductivity and Topography in Electrostrictive Germanium Sulfide Microribbon via Conductive Atomic Force Microscopy.

Layered group IV monochalcogenides are two-dimensional (2D) semiconducting materials with unique crystal structures and novel physical properties. Here, we report the growth of single crystalline GeS microribbons using the chemical vapor transport process. By using conductive atomic force microscopy, we demonstrated that the conductive behavior in the vertical direction was mainly affected by the Schottky barriers between GeS and both electrodes. Furthermore, we found that the topographic and current heterogeneities were significantly different with and without illumination. The topographic deformation and current enhancement were also predicted by our density functional theory (DFT)-based calculations. Their local spatial correlation between the topographic height and current was established. By virtue of 2D fast Fourier transform power spectra, we constructed the holistic spatial correlation between the topographic and current heterogeneity that indicated the diminished correlation with illumination. These findings on layered GeS microribbons provide insights into the conductive and topographic behaviors in 2D materials.

The effective Ni(II) removal of red mud modified chitosan from aqueous solution.

This study used red mud modified with chitosan (RM/CS) as a novel adsorbent to remove Ni(II) ions from an aqueous solution. The adsorbent was characterized by the techniques of the BET method, X-ray diffraction (XRD), and scanning electron microscopy (SEM) analysis. According to the findings, the surface area of RM/CS is nearly doubled compared to CS, from 68.6 to 105.7 m2.g-1. The Ni(II) batch adsorption of RM/CS was performed as a function of pH value, contact time, and volume of adsorbent. Three isotherm adsorption models (Langmuir, Freundlich, and Sips) and three kinetic models (the pseudo-first-order, the pseudo-second-order, and the intra-diffusion models) were fitted with the experimental data to calculate the maximum adsorption capacity and to estimate the uptake in nature. The Langmuir monolayer adsorption capacity for Nickel (II) is 31.66 mg.g-1 at a pH of 6.0, with an adsorption time of 180 min and a temperature of 323 K. The Ni(II) adsorption on RM/CS is the exothermic process and is controlled by the intra-diffusion model.

Threats, challenges and sustainable conservation strategies for freshwater biodiversity.

Increasing human population, deforestation and man-made climate change are likely to exacerbate the negative effects on freshwater ecosystems and species endangerment. Consequently, the biodiversity of freshwater continues to dwindle at an alarming rate. However, this particular topic lacks sufficient attention from conservation ecologists and policymakers, resulting in a dearth of data and comprehensive reviews on freshwater biodiversity, specifically. Despite the widespread awareness of risks to freshwater biodiversity, organized action to reverse this decline has been lacking. This study reviews prospective conservation and management strategies for freshwater biodiversity and their associated challenges, identifying current key threats to freshwater biodiversity. Engineered nanomaterials pose a significant threat to aquatic species, and will make controlling health risks to freshwater biodiversity increasingly challenging in the future. When fish are exposed to nanoparticles, the surface area of their respiratory and ion transport systems can decline to 60% of their total surface area, posing serious health risks. Also, about 50% of freshwater fish species are threatened by climate change, globally. Freshwater biodiversity that is heavily reliant on calcium perishes when the calcium content of their environments degrades, posing another severe threat to world biodiversity. To improve biodiversity, variables such as species diversity, population and water quality, and habitat are essential components that must be monitored continuously. Existing research on freshwater biota and ecosystems is still lacking. Therefore, data collection and the establishment of specialized policies for the conservation of freshwater biodiversity should be prioritized.

Nanomaterials as a sustainable choice for treating wastewater.

Wastewater containing toxic substances is a major threat to the health of both aquatic and terrestrial ecosystems. In order to treat wastewater, nanomaterials are currently being studied intensively due to their unprecedented properties. The unique features of nanoparticles are prompting an increasing number of studies into their use in wastewater treatment. Although several studies have been undertaken in recent years, most of them did not focus on some of the nanomaterials that are now often utilized for wastewater treatment. It is essential to investigate the most recent advances in all the types of nanomaterials that are now frequently employed for wastewater treatment. The recent advancements in common nanomaterials used for sustainable wastewater treatment is comprehensively reviewed in this paper. This paper also thoroughly assesses unique features, proper utilization, future prospects, and current limitations of green nanotechnology in wastewater treatment. Zero-valent metal and metal oxide nanoparticles, especially iron oxides were shown to be more effective than traditional carbon nanotubes (CNTs) for recovering heavy metals in wastewater. Iron oxide achieved 75.9% COD (chemical oxygen demand) removal efficiency while titanium oxide (TiO2) achieved 75.5% COD. Iron nanoparticles attained 72.1% methyl blue removal efficiency. However, since only a few types of nanomaterials have been commercialized, it is important to also focus on the economic feasibility of each nanomaterial. This study found that the large surface area, high reactivity, and strong mechanical properties of nanoparticles means they can be considered as a promising option for successful wastewater treatment.

Randomized p$p$ -values for multiple testing and their application in replicability analysis.

We are concerned with testing replicability hypotheses for many endpoints simultaneously. This constitutes a multiple test problem with composite null hypotheses. Traditional p$p$ -values, which are computed under least favorable parameter configurations (LFCs), are over-conservative in the case of composite null hypotheses. As demonstrated in prior work, this poses severe challenges in the multiple testing context, especially when one goal of the statistical analysis is to estimate the proportion π0$\pi _0$ of true null hypotheses. Randomized p$p$ -values have been proposed to remedy this issue. In the present work, we discuss the application of randomized p$p$ -values in replicability analysis. In particular, we introduce a general class of statistical models for which valid, randomized p$p$ -values can be calculated easily. By means of computer simulations, we demonstrate that their usage typically leads to a much more accurate estimation of π0$\pi _0$ than the LFC-based approach. Finally, we apply our proposed methodology to a real data example from genomics.

Emerging potential of spent coffee ground valorization for fuel pellet production in a biorefinery.

Abstract: The global market for fuel pellets (FPs) has been steadily growing because of a shift to coal substitutes. However, sustainability and the availability of biomass are the main issues. Various kinds of bio-wastes can be valorized through cutting-edge technologies. In the coffee industry, a valuable organic waste called spent coffee grounds (SCGs) is generated in bulk. SCG can be divided into two components, namely spent coffee ground oil and defatted spent coffee grounds (DSCG). SCG and DSCG can be used to produce FPs with excellent higher heating values. This review highlights that burning FPs composed of 100% SCG is not feasible due to the high emission of NOx. Moreover, the combustion is accompanied by a rapid temperature drop due to incomplete combustion which leads to lower boiler combustion efficiencies and increased carbon monoxide emissions. This was because of the low pellet strength and bulk density of the FP. Mixing SCG with other biomass offers improved boiler efficiency and emissions. Some of the reported optimized FPs include 75% SCG + 20% coffee silverskin, 30% SCG + 70% pine sawdust, 90% SCG + 10% crude glycerol, 32% SCG + 23% coal fines + 11% sawdust + 18% mielie husks + 10% waste paper + 6% paper pulp, and 50% SCG + 50% pine sawdust. This review noted the absence of combustion and emissions analyses of DSCG and the need for their future assessment. Valorization of DSCG offers a good pathway to improve the economics of an SCG-based biorefinery where the extracted SCGO can be valorized in other applications. The combustion and emissions of DSCG were not previously reported in detail. Therefore, future investigation of DSCG in boilers is essential to assess the potential of this industry and improve its economics. Supplementary Information: The online version contains supplementary material available at 10.1007/s10668-022-02361-z.

Impact of Tissue Enolase 1 Protein Overexpression in Esophageal Cancer Progression.

Enolase (ENO) 1 is a key glycolytic enzyme and important player in tumorigenesis. ENO1 overexpression has been correlated with tumor progression and/or worse prognosis in several solid malignancies. However, data concerning the impact of ENO1 in cancer conflict. The study correlated local and circulating ENO1 protein levels in esophageal cancer (EC) with clinicopathological data, to assess its potential clinical value. ENO1 expression was analyzed by immunohistochemistry in paired tumor and non-tumor tissue samples from 40 EC cases and mucosal biopsies from 45 Barrett's esophagus (BE) cases, plus in plasma from these patients and 25 matched healthy controls. ENO1 was abnormally elevated in cancer-cell cytoplasm in both EC types, in esophageal squamous cell carcinoma and in adenocarcinoma (EAC), increasing significantly with tumor stage progression and the transition from BE to EAC. EAC patients exhibited significantly lower ENO1 plasma concentrations than normal subjects. Neither local nor systemic ENO1 expression levels were significantly associated with overall survival. These results indicate ENO1 as potential biomarker, delineating a population of patients with Barrett's esophagus at high risk of cancer, and as new therapeutic opportunity in EC patient management. However, further confirmation might be necessary.

Acid-based lignocellulosic biomass biorefinery for bioenergy production: Advantages, application constraints, and perspectives.

The production of chemicals and fuels from renewable biomass with the primary aim of reducing carbon footprints has recently become one of the central points of interest. The use of lignocellulosic biomass for energy production is believed to meet the main criteria of maximizing the available global energy source and minimizing pollutant emissions. However, before usage in bioenergy production, lignocellulosic biomass needs to undergo several processes, among which biomass pretreatment plays an important role in the yield, productivity, and quality of the products. Acid-based pretreatment, one of the existing methods applied for lignocellulosic biomass pretreatment, has several advantages, such as short operating time and high efficiency. A thorough analysis of the characteristics of acid-based biomass pretreatment is presented in this review. The environmental concerns and future challenges involved in using acid pretreatment methods are discussed in detail to achieve clean and sustainable bioenergy production. The application of acid to biomass pretreatment is considered an effective process for biorefineries that aim to optimize the production of desired products while minimizing the by-products.

Impacts of COVID-19 pandemic on the global energy system and the shift progress to renewable energy: Opportunities, challenges, and policy implications.

Being declared a global emergency, the COVID-19 pandemic has taken many lives, threatened livelihoods and businesses around the world. The energy industry, in particular, has experienced tremendous pressure resulting from the pandemic. In response to such a challenge, the development of sustainable resources and renewable energy infrastructure has demonstrated its potential as a promising and effective strategy. To sufficiently address the effect of COVID-19 on renewable energy development strategies, short-term policy priorities should be identified, while mid-term and long-term action plans should be formulated in achieving the well-defined renewable energy targets and progress towards a more sustainable energy future. In this review, opportunities, challenges, and significant impacts of the COVID-19 pandemic on current and future sustainable energy strategies were analyzed in detail; while drawing from experiences in identifying reasonable behaviors, orientating appropriate actions, and policy implications on the sustainable energy trajectory were also mentioned. Indeed, the question is that whether the COVID-19 pandemic will kill us or provide us with a precious lesson on future sustainable energy development.

Molecular typing of conjunctivitis-causing adenoviruses in Hanoi, Vietnam from 2017 to 2019 and complete genome analysis of the most prevalent type (HAdV-8).

Adenoviral conjunctivitis is a common epidemic worldwide. In Vietnam, up to 80,000 patients are infected with adenoviral conjunctivitis annually. However, there are few investigations on the pathogenic adenoviruses that cause conjunctivitis. In total, 120 eye-swab samples were collected from patients with viral conjunctivitis symptoms in Hanoi, Vietnam from 2017 to 2019. Human adenoviruse (HAdV) was detected in 67 samples (55.83%) using polymerase chain reaction amplification of at least one of three HAdV-specific marker genes (hexon, penton, and fiber). Of the 67 HAdV samples, 46 samples could be analyzed by all three marker genes. DNA sequence analysis and phylogenetic tree building based on the three marker genes from the 46 HAdV samples revealed five different HAdV types associated with conjunctivitis in Hanoi, including HAdV-3 (4.3%), HAdV-4 (2.2%), HAdV-8 (89.1%), HAdV-37 (2.2%), and a potential recombinant type between types HAdV-8 and HAdV-3 (2.2%). This showed that HAdV-8 was the most common type identified in Hanoi. Complete genome analysis of HAdV-8 isolated from a Vietnamese patient (VN2017) using Sanger sequencing revealed 34 unique nucleotide changes, indicating that the adenovirus continuously accumulates new mutations. Hence, continuous surveillance of HAdV-8 changes in Vietnam is necessary in the future.

Controllable P- and N-Type Conversion of MoTe2 via Oxide Interfacial Layer for Logic Circuits.

To realize basic electronic units such as complementary metal-oxide-semiconductor (CMOS) inverters and other logic circuits, the selective and controllable fabrication of p- and n-type transistors with a low Schottky barrier height is highly desirable. Herein, an efficient and nondestructive technique of electron-charge transfer doping by depositing a thin Al2 O3 layer on chemical vapor deposition (CVD)-grown 2H-MoTe2 is utilized to tune the doping from p- to n-type. Moreover, a type-controllable MoTe2 transistor with a low Schottky barrier height is prepared. The selectively converted n-type MoTe2 transistor from the p-channel exhibits a maximum on-state current of 10 µA, with a higher electron mobility of 8.9 cm2 V-1 s-1 at a drain voltage (Vds ) of 1 V with a low Schottky barrier height of 28.4 meV. To validate the aforementioned approach, a prototype homogeneous CMOS inverter is fabricated on a CVD-grown 2H-MoTe2 single crystal. The proposed inverter exhibits a high DC voltage gain of 9.2 with good dynamic behavior up to a modulation frequency of 1 kHz. The proposed approach may have potential for realizing future 2D transition metal dichalcogenide-based efficient and ultrafast electronic units with high-density circuit components under a low-dimensional regime.

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.

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.

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.

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, 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 is reported. 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, is designed based on a mesh structure whose elastic restoring force enables the contracted device to spread out. It is 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.