Corrosion mitigation in desalination plants by ammonium-based ionic liquid

Synaptic transistor with multiple biological functions based on metal-organic frameworks combined with the LIF model of a spiking neural network to recognize temporal information

Spiking neural networks (SNNs) have immense potential due to their utilization of synaptic plasticity and ability to take advantage of temporal correlation and low power consumption. The leaky integration and firing (LIF) model and spike-timing-dependent plasticity (STDP) are the fundamental components of SNNs. Here, a neural device is first demonstrated by zeolitic imidazolate frameworks (ZIFs) as an essential part of the synaptic transistor to simulate SNNs. Significantly, three kinds of typical functions between neurons, the memory function achieved through the hippocampus, synaptic weight regulation and membrane potential triggered by ion migration, are effectively described through short-term memory/long-term memory (STM/LTM), long-term depression/long-term potentiation (LTD/LTP) and LIF, respectively. Furthermore, the update rule of iteration weight in the backpropagation based on the time interval between presynaptic and postsynaptic pulses is extracted and fitted from the STDP. In addition, the postsynaptic currents of the channel directly connect to the very large scale integration (VLSI) implementation of the LIF mode that can convert high-frequency information into spare pulses based on the threshold of membrane potential. The leaky integrator block, firing/detector block and frequency adaptation block instantaneously release the accumulated voltage to form pulses. Finally, we recode the steady-state visual evoked potentials (SSVEPs) belonging to the electroencephalogram (EEG) with filter characteristics of LIF. SNNs deeply fused by synaptic transistors are designed to recognize the 40 different frequencies of EEG and improve accuracy to 95.1%. This work represents an advanced contribution to brain-like chips and promotes the systematization and diversification of artificial intelligence.

In situ polymerized ionic liquids in polyester fiber composite membranes for detection of trace oil

In situ trace detection on ultra-clean surfaces is an important technology. The polyester fiber (PF) was introduced to serve as the template, to which the ionic liquids were bonded by hydrogen bonding. Polymerized ionic liquids (PIL) in PF were formed by in situ polymerization with the azodiisobutyronitrile (AIBN) and IL. The trace oil on metal surfaces was enriched by the composite membrane based on similar compatibility principle. The absolute recovery of the trace oil ranged from 91%-99% using this composite membrane. In the extraction samples, desirable linear correlations were obtained for trace oil in the range of 1.25-20 mg/mL. It has been proven that a 1 cm² PIL-PF composite membrane can effectively extract as little as 1 mg of lubricating oil on an ultra-clean metal surface of 0.1 m² with the LOD of 0.9 mg/mL, making it a promising material for in situ detection of trace oil on metal surfaces.

Use of orange peel extract as an inhibitor of stainless steel corrosion during acid washing in a multistage flash desalination plant

Environmentally friendly and cost-effective inhibitors based on orange peel extract (OPE) have been developed for potential applications in ejector tubes of a multistage desalination plant during the acid cleaning process. After conducting tests under both static and hydrodynamic circumstances, it was determined that the inhibitor formulation was effective against corrosion of stainless steel (SS) in 1 M HCl solution at 30 °C under the conditions tested. Electrochemical methods were used along with electron microscopy to collect information on the corrosion inhibition efficiency of the extract. Experiments were conducted for 1, 2, 3, 4, 6, 12, and 24 h, and the performance of OPE was compared to that of a commercial acid corrosion inhibitor to determine which performed better. The results revealed that OPE demonstrated outstanding corrosion inhibition performance compared to the commercial acid corrosion inhibitor. With remarkable inhibition efficacy for up to 24 h under both static and dynamic settings, 0.4% of OPE displayed typical mixed-type corrosion inhibitor behavior in the tested environment. All of the procedures that were used produced results that were in good agreement. Due to the synergistic action between the two compounds, the corrosion inhibition of OPE on SS was improved in 1 M HCl in the presence of iodide ions. In the absence of KI, OPE was found to inhibit SS corrosion at a concentration-dependent rate, with the concentration of OPE being the most significant factor. A study of the adsorption of OPE onto a SS surface revealed that the Langmuir adsorption isotherm controls the process. Based on the quantity of free energy of adsorption observed, it was determined that there is physical contact between the OPE and the surface of SS in this experiment. It was possible to identify the most effective phytochemicals for corrosion inhibition based on the findings of quantum chemical calculations, which were subsequently evaluated in the laboratory.

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Ionic liquid multistate resistive switching characteristics in two terminal soft and flexible discrete channels for neuromorphic computing

By exploiting ion transport phenomena in a soft and flexible discrete channel, liquid material conductance can be controlled by using an electrical input signal, which results in analog neuromorphic behavior. This paper proposes an ionic liquid (IL) multistate resistive switching device capable of mimicking synapse analog behavior by using IL BMIM FeCL₄ and H₂O into the two ends of a discrete polydimethylsiloxane (PDMS) channel. The spike rate-dependent plasticity (SRDP) and spike-timing-dependent plasticity (STDP) behavior are highly stable by modulating the input signal. Furthermore, the discrete channel device presents highly durable performance under mechanical bending and stretching. Using the obtained parameters from the proposed ionic liquid-based synaptic device, convolutional neural network simulation runs to an image recognition task, reaching an accuracy of 84%. The bending test of a device opens a new gateway for the future of soft and flexible brain-inspired neuromorphic computing systems for various shaped artificial intelligence applications.