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Abstract Natural, organic, materials-based artificial synaptic devices have been in the spotlight for wearable/flexible devices due to their lightweight, biocompatibility, and scalability. In this study, an electronic memristive device based on agarose extracted from plants in the Rhodophyceae class was fabricated, and its memory characteristics and analog data processing capabilities were evaluated. The Al/agarose@gold nanoparticle (AuNP) film/indium-tin-oxide (ITO)-structured memristive device exhibited reliable resistive switching characteristics with excellent retention with a large Ron/Roff ratio of 104. Also, analog conductance changes in our device were achieved with power consumption at the pJ level. This notable behavior could be maintained under mechanical deformations from a flat to a 4-mm bent state. In the recognition simulation based on the device's performance, an 91% accuracy and clear digit classification were achieved.

Abstract If the speed of machine learning is to be improved, devices and systems with strong resistances to various types of internal noise, mainly internal thermal noise, are urgently needed. The successful demonstration of a synaptic device is reported based on a polyimide–tungsten disulfide quantum dot (PI‐WS2 QD) nanocomposite that continued to operate normally after simultaneous exposure of a high temperature of 100 °C. Such excellent performance is attributable to the strong quantum confinement effect of WS2 QDs. The working current of the device and its power consumption are on the orders of nanoamperes and femtojoules, respectively. Undoubtedly, such devices will significantly improve the physical performances of machine learning systems and allow the rapid realization of greatly improving machine learning speed.

Abstract Artificial synaptic devices based on natural organic materials are becoming the most desirable for extending their fields of applications to include wearable and implantable devices due to their biocompatibility, flexibility, lightweight, and scalability. Herein, we proposed a zein material, extracted from natural maize, as an active layer in an artificial synapse. The synaptic device exhibited notable digital-data storage and analog data processing capabilities. Remarkably, the zein-based synaptic device achieved recognition accuracy of up to 87% and exhibited clear digit-classification results on the learning and inference test. Moreover, the recognition accuracy of the zein-based artificial synapse was maintained within a difference of less than 2%, regardless of mechanically stressed conditions. We believe that this work will be an important asset toward the realization of wearable and implantable devices utilizing artificial synapses.

Pipe racks are steel structures that support various pipes transporting materials in industrial complexes. The pipes on pipe racks may transport hazardous substances, which imposes limitations on their structural reinforcement. Therefore, this study aimed to propose methods for reinforcing the joints of pipe rack structures through non-welding and non-drilling techniques. The joints of pipe rack structures were connected by end plates. Initially, this study evaluated the capacity of existing end plates in a real-world experiment and conducted cyclic loading tests with two additional reinforcement methods to validate their performance. Finally, finite element analysis was conducted to explore additional variables that were not covered in the experiments, and the optimal reinforcement method that demonstrated the best performance was proposed. [ABSTRACT FROM AUTHOR]

Dynamic bonds can facilitate reversible formation and dissociation of connections in response to external stimuli, endowing materials with shape memory and self-healing capabilities. Temperature is an external stimulus that can be easily controlled through heat. Dynamic covalent bonds in response to temperature can reversibly connect, exchange, and convert chains in the polymer. In this review, we introduce dynamic covalent bonds that operate without catalysts in various temperature ranges. The basic bonding mechanism and the kinetics are examined to understand dynamic covalent chemistry reversibly performed by equilibrium control. Furthermore, a recent synthesis method that implements dynamic covalent coupling based on various polymers is introduced. Dynamic covalent bonds that operate depending on temperature can be applied and expand the use of polymers, providing predictions for the development of future smart materials. [ABSTRACT FROM AUTHOR]

Malaria is one of the most prevalent diseases worldwide with high incidence and mortality. Among the five species that can infect humans, Plasmodium ovale morphologically resembles Plasmodium vivax, resulting in misidentification and confusion in diagnosis, and is responsible for malarial disease relapse due to the formation of hypnozoites. P. ovale receives relatively less attention compared to other major parasites, such as P. falciparum and P. vivax, primarily due to its lower pathogenicity, mortality rates, and prevalence rates. To efficiently produce lactate dehydrogenase (LDH), a major target for diagnosing malaria, this study used three Escherichia coli strains, BL21(DE3), BL21(DE3)pLysS, and Rosetta(DE3), commonly used for recombinant protein production. These strains were characterized to select the optimal strain for P. ovale LDH (PoLDH) production. Gene cloning for recombinant PoLDH production and transformation of the three strains for protein expression were performed. The optimal PoLDH overexpression and washing buffer conditions in nickel-based affinity chromatography were established to ensure high-purity PoLDH. The yields of PoLDH expressed by the three strains were as follows: BL21(DE3), 7.6 mg/L; BL21(DE3)pLysS, 7.4 mg/L; and Rosetta(DE3), 9.5 mg/L. These findings are expected to be highly useful for PoLDH-specific diagnosis and development of antimalarial therapeutics. [ABSTRACT FROM AUTHOR]