Fabrication of graphene-based sensor for exposure to different chemicals.

Opto-chemical sensors are the most significant type of sensors that are widely used to detect a variety of volatile organic compounds and chemicals. This research work demonstrates the fabrication and characterization of an opto-chemical sensor based on a graphene thin film. A 300 nm graphene thin film was deposited on clean glass with the help of RF magnetron sputtering. The structure, surface and quality of the graphene thin film were characterized using XRD, SEM and Raman spectroscopy. For optical characterization, the thin film was exposed to IPA, acetone and toluene (separately) for five, ten and fifteen minutes. The optical transmission was then observed via UV-NIR spectroscopy in the near-infrared range (900 to 1450 nm). The thin film of graphene has expressed a sharp response time and recovery time with high sensitivity for each chemical. However, by comparing the output of the graphene thin film in response to each chemical, it was observed that graphene thin film has a better transmission and sensing rate for exposure to toluene.

A Machine Learning Architecture Replacing Heavy Instrumented Laboratory Tests: In Application to the Pullout Capacity of Geosynthetic Reinforced Soils.

For economical and sustainable benefits, conventional retaining walls are being replaced by geosynthetic reinforced soil (GRS). However, for safety and quality assurance purposes, prior tests of pullout capacities of these materials need to be performed. Conventionally, these tests are conducted in a laboratory with heavy instruments. These tests are time-consuming, require hard labor, are prone to error, and are expensive as a special pullout machine is required to perform the tests and acquire the data by using a lot of sensors and data loggers. This paper proposes a data-driven machine learning architecture (MLA) to predict the pullout capacity of GRS in a diverse environment. The results from MLA are compared with actual laboratory pullout capacity tests. Various input variables are considered for training and testing the neural network. These input parameters include the soil physical conditions based on water content and external loading applied. The soil used is a locally available weathered granite soil. The input data included normal stress, soil saturation, displacement, and soil unit weight whereas the output data contains information about the pullout strength. The data used was obtained from an actual pullout capacity test performed in the laboratory. The laboratory test is performed according to American Society for Testing and Materials (ASTM) standard D 6706-01 with little modification. This research shows that by using machine learning, the same pullout resistance of a geosynthetic reinforced soil can be achieved as in laboratory testing, thus saving a lot of time, effort, and money. Feedforward backpropagation neural networks with a different number of neurons, algorithms, and hidden layers have been examined. The comparison of the Bayesian regularization learning algorithm with two hidden layers and 12 neurons each showed the minimum mean square error (MSE) of 3.02 × 10-5 for both training and testing. The maximum coefficient of regression (R) for the testing set is 0.999 and the training set is 0.999 for the prediction interval of 99%.

Vision beyond the Field-of-View: A Collaborative Perception System to Improve Safety of Intelligent Cyber-Physical Systems.

Cyber-physical systems (CPSs) that interact with each other to achieve common goals are known as collaborative CPSs. Collaborative CPSs can achieve complex goals that individual CPSs cannot achieve on their own. One of the examples of collaborative CPSs is the vehicular cyber-physical systems (VCPSs), which integrate computing and physical resources to interact with each other to improve traffic safety, situational awareness, and efficiency. The perception system of individual VCPS has limitations on its coverage and detection accuracy. For example, the autonomous vehicle's sensor cannot detect occluded objects and obstacles beyond its field of view. The VCPS can combine its own data with other collaborative VCPSs to enhance perception, situational awareness, accuracy, and traffic safety. This paper proposes a collaborative perception system to detect occluded objects through the camera sensor's image fusion and stitching technique. The proposed collaborative perception system combines the perception of surrounding autonomous driving systems (ADSs) that extends the detection range beyond the field of view. We also applied logistic chaos map-based encryption in our collaborative perception system in order to avoid the phantom information shared by malicious vehicles and improve safety in collaboration. It can provide the real-time perception of occluded objects, enabling safer control of ADSs. The proposed collaborative perception can detect occluded objects and obstacles beyond the field of view that individual VCPS perception systems cannot detect, improving the safety of ADSs. We investigated the effectiveness of collaborative perception and its contribution toward extended situational awareness on the road in the simulation environment. Our simulation results showed that the average detection rate of proposed perception systems was 45.4% more than the perception system of an individual ADS. The safety analysis showed that the response time was increased up to 1 s, and the average safety distance was increased to 1.2 m when the ADSs were using collaborative perception compared to those scenarios in which the ADSs were not using collaborative perception.

SafeSoCPS: A Composite Safety Analysis Approach for System of Cyber-Physical Systems.

The System of Cyber-Physical Systems (SoCPS) comprises several independent Cyber-Physical Systems (CPSs) that interact with each other to achieve a common mission that the individual systems cannot achieve on their own. SoCPS are rapidly gaining attention in various domains, e.g., manufacturing, automotive, avionics, healthcare, transportation, and more. SoCPS are extremely large, complex, and safety-critical. As these systems are safety-critical in nature, it is necessary to provide an adequate safety analysis mechanism for these collaborative SoCPS so that the whole network of these CPSs work safely. This safety mechanism must include composite safety analysis for a network of collaborative CPS as a whole. However, existing safety analysis techniques are not built for analyzing safety for dynamically forming networks of CPS. This paper introduces a composite safety analysis approach called SafeSoCPS to analyze hazards for a network of SoCPS. In SafeSoCPS, we analyze potential hazards for the whole network of CPS and trace the faults among participating systems through a fault propagation graph. We developed a tool called SoCPSTracer to support the SafeSoCPS approach. Human Rescue Robot System-a collaborative system-is taken as a case study to validate our proposed approach. The result shows that the SafeSoCPS approach enables us to identify 18 percent more general faults and 63 percent more interaction-related faults in a network of a SoCPS.

Low cost, robust, environmentally friendly, wood supported 3D-hierarchical Cu3SnS4 for efficient solar powered steam generation.

Solar steam generation has great potential in alleviating freshwater crises, particularly in regions with accessible seawater and abundant insolation. Inexpensive, efficient, and eco-friendly photothermal materials are desired to fabricate sunlight-driven evaporation devices. Here, we have designed an economical strategy to fabricate a high-performance wood-based solar steam generation device. In current study, 3D-hierarchical Cu3SnS4 has been loaded on wood substrates of variable sizes via an in-situ solvothermal method. Considering the water transportation capacity and thermal insulation property of wood, an enhanced light absorption was achieved by a uniform coating of Cu3SnS4 on the inside and outside of the 3D porous structure of the wood. Thanks for the synergistic effect of Cu3SnS4 and wood substrate, the obtained composite endorsed high-performance solar steam generation with a steam generation efficiency of 90% and an evaporation rate as high as 1.35 kg m-2h-1 under one sun.

Chemical Coupled PEDOT:PSS/Si Electrode: Suppressed Electrolyte Consumption Enables Long-Term Stability.

Huge volume changes of Si during lithiation/delithiation lead to regeneration of solid-electrolyte interphase (SEI) and consume electrolyte. In this article, γ-glycidoxypropyl trimethoxysilane (GOPS) was incorporated in Si/PEDOT:PSS electrodes to construct a flexible and conductive artificial SEI, effectively suppressing the consumption of electrolyte. The optimized electrode can maintain 1000 mAh g-1 for nearly 800 cycles under limited electrolyte compared with 40 cycles of the electrodes without GOPS. Also, the optimized electrode exhibits excellent rate capability. The use of GOPS greatly improves the interface compatibility between Si and PEDOT:PSS. XPS Ar+ etching depth analysis proved that the addition of GOPS is conducive to forming a more stable SEI. A full battery assembled with NCM 523 cathode delivers a high energy density of 520 Wh kg-1, offering good stability.

Current Strategies and Novel Drug Approaches for Alzheimer Disease.

Alzheimer's Disease (AD) is a chronic, devastating dysfunction of neurons in the brain leading to dementia. It mainly arises due to neuronal injury in the cerebral cortex and hippocampus area of the brain and is clinically manifested as a progressive mental failure, disordered cognitive functions, personality changes, reduced verbal fluency and impairment of speech. The pathology behind AD is the formation of intraneuronal fibrillary tangles, deposition of amyloid plaque and decline in choline acetyltransferase and loss of cholinergic neurons. Tragically, the disease cannot be cured, but its progression can be halted. Various cholinesterase inhibitors available in the market like Tacrine, Donepezil, Galantamine, Rivastigmine, etc. are being used to manage the symptoms of Alzheimer's disease. The paper's objective is to throw light not only on the cellular/genetic basis of the disease, but also on the current trends and various strategies of treatment including the use of phytopharmaceuticals and nutraceuticals. Enormous literature survey was conducted and published articles of PubMed, Scifinder, Google Scholar, Clinical Trials.org and Alzheimer Association reports were studied intensively to consolidate the information on the strategies available to combat Alzheimer's disease. Currently, several strategies are being investigated for the treatment of Alzheimer's disease. Immunotherapies targeting amyloid-beta plaques, tau protein and neural pathways are undergoing clinical trials. Moreover, antisense oligonucleotide methodologies are being approached as therapies for its management. Phytopharmaceuticals and nutraceuticals are also gaining attention in overcoming the symptoms related to AD. The present review article concludes that novel and traditional therapies simultaneously promise future hope for AD treatment.

Ion-Cross-Linking-Promoted High-Performance Si/PEDOT:PSS Electrodes: The Importance of Cations' Ionic Potential and Softness Parameters.

PEDOT: PSS has been studied as a silicon-based binder due to its inherent superior electricity and electrochemical stability. However, it cannot effectively alleviate the huge volume changes of silicon during lithiation/delithiation due to its linear structure, resulting in poor cycling stability. Ion-cross-linking is a usual method to cross-link linear polymers into 3D structures. In this paper, multivalent cations of the 5th period and Group 2 cross-linked PEDOT:PSS were applied as silicon anode binders and studied systematically. It was found that the variation trend of viscosity and conductivity of PEDOT:PSS after cross-linking was consistent with that of ionic potential and softness parameters of multivalent cations. The mesostructure of a binder after cross-linking is influenced by the solubility product constant of sulfites or hydroxides of cations and the growth characteristics of crystals. An Sn4+-cross-linked binder displayed increased viscosity and electrical conductivity and higher reduced modulus and hardness due to its positive softness parameter and higher ion potential. The Si electrode with the Sn4+-cross-linked binder showed improved cycling stability (1876.4 mAh g-1 compared with 1068.4 mAh g-1 of the electrode with the pure PEDOT:PSS binder after 100 cycles) and superior rate capability (∼800 mAh g-1 at an ultrahigh current density of 8.0 A g-1).

Glycerol-crosslinked PEDOT:PSS as bifunctional binder for Si anodes: Improved interfacial compatibility and conductivity.

Developing conductive polymer binders is a new way to enhance the electric connectivity and mechanical contact of Si based anode material. While the linear structure of commercial PEDOT:PSS cannot effectively alleviate the volume expansion of Si. Herein, glycerol was introduced as a cross-linker to PEDOT:PSS binder for Si anodes, which can further improve the interfacial compatibility between silicon and PEDOT:PSS. After crosslinking, the peel force increased 2 times. As a result, the Si nanoparticles anode with the glycerol-crosslinked binder exhibited a high reversible capacity of 1951.5 mAh g-1 after 200 cycles at 0.5 A g-1 and superior rate capability (804 mAh g-1 at a high current of 8.0 A g-1) for the inherent superior conductivity of PEDOT:PSS.

Utilizing the Space-Charge Region of the FeNi-LDH/CoP p-n Junction to Promote Performance in Oxygen Evolution Electrocatalysis.

The modulation of electron density is an effective option for efficient alternative electrocatalysts. Here, p-n junctions are constructed in 3D free-standing FeNi-LDH/CoP/carbon cloth (CC) electrode (LDH=layered double hydroxide). The positively charged FeNi-LDH in the space-charge region can significantly boost oxygen evolution reaction. Therefore, the j at 1.485 V (vs. RHE) of FeNi-LDH/CoP/CC achieves ca. 10-fold and ca. 100-fold increases compared to those of FeNi-LDH/CC and CoP/CC, respectively. Density functional theory calculation reveals OH- has a stronger trend to adsorb on the surface of FeNi-LDH side in the p-n junction compared to individual FeNi-LDH further verifying the synergistic effect in the p-n junction. Additionally, it represents excellent activity toward water splitting. The utilization of heterojunctions would open up an entirely new possibility to purposefully regulate the electronic structure of active sites and promote their catalytic activities.

Does globalization increase the ecological footprint? Empirical evidence from Malaysia.

This study focuses to investigate the relationship between globalization and the ecological footprint for Malaysia from 1971 to 2014. The results of the Bayer and Hanck cointegration test and the ARDL bound test show the existence of cointegration among variables. The findings disclose that globalization is not a significant determinant of the ecological footprint; however, it significantly increases the ecological carbon footprint. Energy consumption and economic growth stimulate the ecological footprint and carbon footprint in Malaysia. Population density reduces the ecological footprint and carbon footprint. Further, financial development mitigates the ecological footprint. The causality results disclose the feedback hypothesis between energy consumption and economic growth in the long run and short run.

Pronounced Impact of p-Type Carriers and Reduction of Bandgap in Semiconducting ZnTe Thin Films by Cu Doping for Intermediate Buffer Layer in Heterojunction Solar Cells.

Stabilized un-doped Zinc Telluride (ZnTe) thin films were grown on glass substrates under vacuum using a closed space sublimation (CSS) technique. A dilute copper nitrate solution (0.1/100 mL) was prepared for copper doping, known as an ion exchange process, in the matrix of the ZnTe thin film. The reproducible polycrystalline cubic structure of undoped and the Cu doped ZnTe thin films with preferred orientation (111) was confirmed by X-rays diffraction (XRD) technique. Lattice parameter analyses verified the expansion of unit cell volume after incorporation of Cu species into ZnTe thin films samples. The micrographs of scanning electron microscopy (SEM) were used to measure the variation in crystal sizes of samples. The energy dispersive X-rays were used to validate the elemental composition of undoped and Cu-doped ZnTe thin films. The bandgap energy 2.24 eV of the ZnTe thin film decreased after doping Cu to 2.20 eV and may be due to the introduction of acceptors states near to valance band. Optical studies showed that refractive index was measured from 2.18 to 3.24, whereas thicknesses varied between 220 nm to 320 nm for un-doped and Cu doped ZnTe thin film, respectively, using the Swanepoel model. The oxidation states of Zn+2, Te+2, and Cu+1 through high resolution X-ray photoelectron spectroscopy (XPS) analyses was observed. The resistivity of thin films changed from ~107 Ω·cm or undoped ZnTe to ~1 Ω·cm for Cu-doped ZnTe thin film, whereas p-type carrier concentration increased from 4 × 109 cm-2 to 1.4 × 1011 cm-2, respectively. These results predicted that Cu-doped ZnTe thin film can be used as an ideal, efficient, and stable intermediate layer between metallic and absorber back contact for the heterojunction thin film solar cell technology.