Thin crystalline silicon solar cells have the potential to achieve high efficiency due to the potential for increased voltage. Thin silicon wafers are fragile; therefore, means of support must be provided. This paper reports the design, development, and analysis of an 18-μm crystalline silicon solar cell electrically integrated with a steel alloy substrate. This ultrathin silicon is epitaxially grown on porous silicon and then transferred onto the steel substrate. This method allows the independent processing of each surface. The steel substrate enables robust handling and provides a conductive back plane. Three groups of cells with planar and textured structures are compared; significant improvements in J _sc , V _oc , and fill factor (FF) are achieved. The best cell shows an efficiency of 16.8% with an open-circuit voltage of 632 mV and a short-circuit current density of 34.5 mA/cm ² .

This paper reports the first successful fabrication of an ITO/silkworm hemolymph/Al bio-memristor using silkworm hemolymph as the active layer. Experiments demonstrate that the silkworm hemolymph bio-memristor is a nonvolatile rewritable bipolar memory device with a current switching ratio exceeding 10³. The state of the bio-memristor can be retained for more than 10⁴ seconds and remains stable for at least 500 cycles. Tests of 1/f noise have shown that the resistance switching characteristics of the silkworm hemolymph bio-memristor are related to the formation and breaking of conductive filaments, which result from the migration of oxygen ions and the oxidation and reduction of metal cations in the silkworm hemolymph film. The naturally non-toxic silkworm hemolymph offers advantages for human health, environmental protection, and biocompatibility. The proposed nonvolatile rewritable bio-memristor based on silkworm hemolymph possesses great application potential.

Owing to the highly parallel network structure of the biological neural network and its triggered processing mode, tactile sensory neurons can realize the perception of external signals and the functions of perception, memory, and data processing by adjusting the synaptic weight. In this paper, a piezoresistive pressure sensor is combined with a memristor to design an artificial tactile sensory neuron. The polyurethane sponge sensor has excellent sensitivity and can convert physical stimuli into electrical signals, and the chitosan‐based memristor has stable bipolar resistive switching characteristics, allowing further information to be memorized and processed. The neuron can respond to tactile stimuli of different degrees, durations, and frequencies; realize potentiation/depression modulation, paired‐pulse facilitation, and spike‐timing‐dependent plasticity; exhibit spike‐rate‐dependent plasticity; and store and erase tactile information through memistor state switching, which has great application potential in biological sensing systems.

Nanocarbon materials have the advantages of biocompatibility, thermal stability and chemical stability and have shown excellent electrical properties in electronic devices. In this study, Al/MWCNT:GQD/ITO memristors with rewritable nonvolatile properties were prepared based on composites consisting of multiwalled carbon nanotubes (MWCNTs) and graphene quantum dots (GQDs). The switching current ratio of such a device can be tuned in two ways. Due to the ultraviolet light sensitivity of GQDs, when the dielectric material is illuminated by ultraviolet light, the charge capture ability of the GQDs decreases with an increasing duration of illumination, and the switching current ratio of the device also decreases with an increasing illumination duration (10³–10). By exploiting the charge capture characteristics of GQDs, the trap capture level can be increased by increasing the content of GQDs in the dielectric layer. The switching current ratio of the device increases with increasing GQD content (10–10³). The device can be programmed and erased more than 100 times; the programmable switching state can withstand 10⁵ read pulses, and the retention time is more than 10⁴ s. This memristor has a simple structure, low power consumption, and enormous application potential for data storage, artificial intelligence, image processing, artificial neural networks, and other applications.