Large-scale soil application of hydrochar: Reducing its polycyclic aromatic hydrocarbon content and toxicity by heating.

The beneficial roles of hydrochar in carbon sequestration and soil improvement are widely accepted. Despite few available reports regarding polycyclic aromatic hydrocarbons (PAHs) generated during preparation, their potential negative impacts on ecosystems remain a concern. A heating treatment method was employed in this study for rapidly removing PAHs and reducing the toxicity of corn stover-based hydrochar (CHC). The result showed total PAHs content (∑PAH) decreased and then sharply increased within the temperature range from 150 °C to 400 °C. The ∑PAH and related toxicity in CHC decreased by more than 80% under 200 °C heating temperature, compared with those in the untreated sample, representing the lowest microbial toxicity. Benzo(a)pyrene produced a significant influence on the ecological toxicity of the hydrochar among the 16 types of PAHs. The impact of thermal treatment on the composition, content, and toxicity of PAHs was significantly influenced by the adsorption, migration, and desorption of PAHs within hydrochar pores, as well as the disintegration and aggregation of large molecular polymers. The combination of hydrochar with carbonized waste heat and exhaust gas collection could be a promising method to efficiently and affordably reduce hydrochar ecological toxicity.

Analysis of the adsorption and fixation process of ammonium nitrogen in arable soil by biochar based on molecular dynamics simulation.

The ammonia nitrogen in arable land soil is susceptible to environmental and anthropogenic influences, leading to nutrient loss. This study utilized indoor soil column leaching experiments, combined with adsorption mathematical models, traditional characterization methods, and molecular dynamics simulation methods, to analyze the effects of biochar on changes in ammonium ions in different soil layers and leachate of arable land soil. The study found that applying biochar at a ratio of 10 % to arable land soil could effectively increase the ammonium ion content in the 0-10 cm soil layer by 1.57-2.36 times and reduce loss by 44.83-72.27 %. The adsorption and fixation process of biochar is controlled by electrostatic attraction and ion exchange processes. Interactions between molecules, electrostatic forces, and system internal energy also have certain effects on the process. Near the structure of C6H12O6, there are low-energy adsorption sites for ammonium ions, which can provide the energy required for electrostatic attraction. Structures such as C5H10O5, C-S-H, C-SO3, and C4H7NO4 respectively play roles in physical adsorption or chemical adsorption through displacement reactions, electron exchange, and other forms. The adsorption free energy is -394,590.84 kcal/mol, indicating stable adsorption and a process that tends to interact with the biochar surface. This study addresses issues such as the easy loss of ammonia nitrogen in arable land soil and the unclear adsorption mechanism of biochar on ammonium ions, providing a theoretical basis for the field of environmental science.

Nutrient-rich hydrothermal carbon production by exogenous nutrients combined with seaweed internal water.

As a product of hydrothermal carbonization (HTC) technology, hydrothermal carbon has shown excellent application potential in soil improvement, greenhouse gas reduction and pollution remediation. Since a large amount of water and biomass are directly used as reaction media, hydrothermal carbon produced by traditional HTC possesses poor nutrient properties and accompanied by the generation of toxic and hazardous wastewater. Here, a versatile and easily scalable strategy has been demonstrated for the one-step production of industrial nutrient-rich hydrothermal carbon (NRHC) by combining the exogenous nutrients with seaweed internal water. During the reaction process, exogenous nutrients (NH4H2PO3, KNO3, CO(NH2)2) participated in the HTC reaction and were uniformly distributed on the surface of hydrothermal carbon through surface complexation precipitation, ion exchange, and electrostatic interactions. Simulations based on density functional theory revealed that NRHC produced in presence of exogenous nutrients possessed more active sites and surface charges. Moreover, the adsorbent and adsorbate were simultaneously affected by intermolecular forces, electrostatic forces, and internal energy of the system, and the thermodynamics of adsorption process was more stable. Compared with no exogenous nutrient involvement, NRHC produced by exogenous nutrients showed 2.12, 18.56, and 25.69 times increase in the N, P, and K content. The length of the seed germination root system increased by 4.3-5.9 times, which met the standards set for agricultural fertilizer. Due to increased yield per unit volume and reduced wastewater generation, the cost of NRHC production reduced by 47.83-58.23 per cent and profit enhanced by 1.56-1.68 times, as compared to traditional HTC. This low-cost streamlined process provides a new strategy for large-scale production and direct application of hydrothermal carbon.

Description of the whole mitochondrial genome of Bhatia longiradiata (Hemiptera, Cicadellidae, Deltocephalinae: Drabescini) and phylogenetic relationship.

BACKGROUND: Mitochondrial genomes are extremely conserved in genetic processes and valuable molecular indications for phylogenetic and evolutionary examination, but the mitochondrial genome of Bhatia has not yet been reported. OBJECTIVE: The target of this writing was to clarify the structural module of the mitochondrial genes of Bhatia longiradiata, verify the monophyletic of Drabescini, and explore the phylogenetic relationship between Drabescini with other leafhoppers. METHODS: We performed sequencing and explanatory note of the mitochondrion of Bhatia longiradiata. The phylogeny relation was created by ML and Bayesian approaches using three dissimilar datasets (PCG12, PCG12rRNA, and AA), which were constructed to discuss the phylogenetic status of Bhatia longiradiata. RESULTS: To report the architectural feature of the chondriosome of Bhatia longiradiata is a seal double-stranded annular molecule with 16,122 bp measurement and cover typically 37 genes. Several tandem repetitive units were observed in an AT enrichment area. The analysis showed that the branching relationships among the six trees were generally consistent, and each of the subfamilies was individually clustered into a monophyletic group within Cicadellidae. Bhatia longiradiata and other members of the Drabescini were aggregated into a clade that was situated within the Deltocephalinae. CONCLUSION: The mitochondrial genome of Bhatia longiradiata covers 37 typical genes and a control region, which covers six tandem repeats. All species of Drabescini procedure a clade within Deltocephalinae. Drabescini and Scaphoideini form a branch and show a sister relationship with strong support. Therefore, we support the relegation of Selenocephalinae to a clan within Deltocephalinae.

Compact artificial neuron based on anti-ferroelectric transistor.

Neuromorphic machines are intriguing for building energy-efficient intelligent systems, where spiking neurons are pivotal components. Recently, memristive neurons with promising bio-plausibility have been developed, but with limited reliability, bulky capacitors or additional reset circuits. Here, we propose an anti-ferroelectric field-effect transistor neuron based on the inherent polarization and depolarization of Hf0.2Zr0.8O2 anti-ferroelectric film to meet these challenges. The intrinsic accumulated polarization/spontaneous depolarization of Hf0.2Zr0.8O2 films implements the integration/leaky behavior of neurons, avoiding external capacitors and reset circuits. Moreover, the anti-ferroelectric neuron exhibits low energy consumption (37 fJ/spike), high endurance (>1012), high uniformity and high stability. We further construct a two-layer fully ferroelectric spiking neural networks that combines anti-ferroelectric neurons and ferroelectric synapses, achieving 96.8% recognition accuracy on the Modified National Institute of Standards and Technology dataset. This work opens the way to emulate neurons with anti-ferroelectric materials and provides a promising approach to building high-efficient neuromorphic hardware.

The effect of biochar on the migration theory of nutrient ions.

As the acidification of arable soils increases, the utilization of nutrient ions such as N, P, and K decreases substantially. It causes environmental pollution and reduces crop yields. Through previous studies, acidified soil amendments have problems such as easy-retrograde and unclear mechanism. Therefore, in this study, biochar prepared by pyrolysis using peanut shells was used as a green amendment for acidified soil. Biochar with 0, 5 and 10 % biochar ratios were applied to the acidified soil, and the improvement and mechanism were investigated via experiments and software simulations. Analysis of the software simulation results revealed that biochar had the highest unit adsorption of K+ through physical adsorption at 820.38 mg/g. This was followed by PO43-, NO3-, and NH4+ as 270.51, 235.65 and 130.93 mg/g, respectively. These ions were controlled by both electrostatic and ion-exchange adsorption processes. During the improvement, the 10 % biochar ratio group performed the best with a 65.32 % reduction in the outlet volume, and the accumulated levels of nutrient ions in the leachate dropped by 48.40-68.28 % and increased by 437.80-913.87 % in the surface soil. Nutrient ion levels decreased gradually with the increase of soil depth, which agreed with the software simulation results. This study found that applying biochar to acidified soils can provide a solution to low nutrient utilization efficiency and unclear improvement mechanism of acidified soils, and provide a partial theoretical basis for the large-scale application of biochar. Future research on biochar for soil carbon sink and microbial expansion can be strengthened to contribute to environmental protection and multi-level utilization of energy.

Artificial Visual Perception Nervous System Based on Low-Dimensional Material Photoelectric Memristors.

The visual perception system is the most important system for human learning since it receives over 80% of the learning information from the outside world. With the exponential growth of artificial intelligence technology, there is a pressing need for high-energy and area-efficiency visual perception systems capable of processing efficiently the received natural information. Currently, memristors with their elaborate dynamics, excellent scalability, and information (e.g., visual, pressure, sound, etc.) perception ability exhibit tremendous potential for the application of visual perception. Here, we propose a fully memristor-based artificial visual perception nervous system (AVPNS) which consists of a quantum-dot-based photoelectric memristor and a nanosheet-based threshold-switching (TS) memristor. We use a photoelectric and a TS memristor to implement the synapse and leaky integrate-and-fire (LIF) neuron functions, respectively. With the proposed AVPNS we successfully demonstrate the biological image perception, integration and fire, as well as the biosensitization process. Furthermore, the self-regulation process of a speed meeting control system in driverless automobiles can be accurately and conceptually emulated by this system. Our work shows that the functions of the biological visual nervous system may be systematically emulated by a memristor-based hardware system, thus expanding the spectrum of memristor applications in artificial intelligence.

FangTianSim: High-Level Cycle-Accurate Resistive Random-Access Memory-Based Multi-Core Spiking Neural Network Processor Simulator.

Realization of spiking neural network (SNN) hardware with high energy efficiency and high integration may provide a promising solution to data processing challenges in future internet of things (IoT) and artificial intelligence (AI). Recently, design of multi-core reconfigurable SNN chip based on resistive random-access memory (RRAM) is drawing great attention, owing to the unique properties of RRAM, e.g., high integration density, low power consumption, and processing-in-memory (PIM). Therefore, RRAM-based SNN chip may have further improvements in integration and energy efficiency. The design of such a chip will face the following problems: significant delay in pulse transmission due to complex logic control and inter-core communication; high risk of digital, analog, and RRAM hybrid design; and non-ideal characteristics of analog circuit and RRAM. In order to effectively bridge the gap between device, circuit, algorithm, and architecture, this paper proposes a simulation model-FangTianSim, which covers analog neuron circuit, RRAM model and multi-core architecture and its accuracy is at the clock level. This model can be used to verify the functionalities, delay, and power consumption of SNN chip. This information cannot only be used to verify the rationality of the architecture but also guide the chip design. In order to map different network topologies on the chip, SNN representation format, interpreter, and instruction generator are designed. Finally, the function of FangTianSim is verified on liquid state machine (LSM), fully connected neural network (FCNN), and convolutional neural network (CNN).

A Habituation Sensory Nervous System with Memristors.

The sensory nervous system (SNS) builds up the association between external stimuli and the response of organisms. In this system, habituation is a fundamental characteristic that filters out irrelevantly repetitive information and makes the SNS adapt to the external environment. To emulate this critical process in electronic devices, a Lix SiOy -based memristor (TiN/Lix SiOy /Pt) is developed where the temporal response under repetitive stimulation is similar to that of habituation. By connecting this synaptic device to a leaky integrate-and-fire neuron based on a Ag/SiO2 :Ag/Au memristor, a fully memristive SNS with habituation is experimentally demonstrated. Finally, a habituation spiking neural network based on the SNS is built and its application in obstacle avoidance for robot navigation is successfully presented. The results provide that a direct emulation of the biologically inspired learning process by memristors could be a sound choice for neuromorphic hardware implementation.

Oxide-Based Electrolyte-Gated Transistors for Spatiotemporal Information Processing.

Spiking neural networks (SNNs) sharing large similarity with biological nervous systems are promising to process spatiotemporal information and can provide highly time- and energy-efficient computational paradigms for the Internet-of-Things and edge computing. Nonvolatile electrolyte-gated transistors (EGTs) provide prominent analog switching performance, the most critical feature of synaptic element, and have been recently demonstrated as a promising synaptic device. However, high performance, large-scale EGT arrays, and EGT application for spatiotemporal information processing in an SNN are yet to be demonstrated. Here, an oxide-based EGT employing amorphous Nb2 O5 and Lix SiO2 is introduced as the channel and electrolyte gate materials, respectively, and integrated into a 32 × 32 EGT array. The engineered EGTs show a quasi-linear update, good endurance (106 ) and retention, a high switching speed of 100 ns, ultralow readout conductance (<100 nS), and ultralow areal switching energy density (20 fJ µm-2 ). The prominent analog switching performance is leveraged for hardware implementation of an SNN with the capability of spatiotemporal information processing, where spike sequences with different timings are able to be efficiently learned and recognized by the EGT array. Finally, this EGT-based spatiotemporal information processing is deployed to detect moving orientation in a tactile sensing system. These results provide an insight into oxide-based EGT devices for energy-efficient neuromorphic computing to support edge application.

Artificial Synapse Based on van der Waals Heterostructures with Tunable Synaptic Functions for Neuromorphic Computing.

Two-dimensional (2D) materials and van der Waals heterostructures have attracted tremendous attention because of their appealing electronic, mechanical, and optoelectronic properties, which offer the possibility to extend the range of functionalities for diverse potential applications. Here, we fabricate a novel multiterminal device with dual-gate based on 2D material van der Waals heterostructures. Such a multiterminal device exhibited excellent nonvolatile multilevel resistance switching performance controlled by the source-drain voltage and back-gate voltage. Based on these features, heterosynaptic plasticity, in which the synaptic weight can be tuned by another modulatory interneuron, has been mimicked. A tunable analogue weight update (both on/off ratio and update nonlinearity) of synapse with high speed (50 ns) and low energy (∼7.3 fJ) programming has been achieved. These results demonstrate the great potential of the artificial synapse based on van der Waals heterostructures for neuromorphic computing.