Recent Patents on Nanotechnology - Online First

This paper provides an in-depth look at the latest developments in dye-sensitized solar cell (DSSC) technology. It focuses on the use of special materials, like polyaniline (PANI), graphene oxide (GO), and carbon nanotubes (CNTs). These materials improve the efficiency and stability of solar cells, and this study offers significant insights into their characteristics and practical uses. This article examines major trends in material selection, structural optimization, and manufacturing procedures by juxtaposing results from scientific literature with advancements in the patent arena, addressing the issues of developing next-generation solar cell designs. We examine the synergistic effects of PANI's stability, GO's electrical conductivity, and CNTs' mechanical strength, highlighting their roles in enhancing light absorption, charge transfer efficiency, and overall device longevity. Bibliometric data from sites, like Scopus and Lens.org, indicate substantial advancements in energy conversion efficiency and decreases in charge transfer resistance. Patents, like WO 2020 and EP3824-B1, illustrate the increasing significance of flexibility, resilience, and scalability in solar cell designs. Biopolymer-based electrolytes made from chitosan, guar gum, and starch are examples of sustainable solutions that show better ionic conductivity and mechanical stability, making them eco-friendly choices. This paper highlights the significance of nano and microfillers in enhancing electron mobility and minimizing resistive losses. Practical implementations, including photovoltaic chargers and flexible solar panels, illustrate the conversion of theoretical advancements into functional technologies. The study delineates future research avenues, promoting the utilization of nanocomposites and catalytic materials to enhance solar cell performance and thus facilitate sustainable and scalable energy solutions to address escalating global energy demands.

The increase in computational power demand led by the development of Artificial Intelligence is rapidly becoming unsustainable. New paradigms of computation, which potentially differ from digital computation, together with novel hardware architecture and devices, are anticipated to reduce the exorbitant energy demand for data-processing tasks. Memristive systems with resistive switching behavior are under intense research, given their prominent role in the fabrication of memory devices that promise the desired hardware revolution in our intensive data-driven era. They are suggested to provide the hardware substrate to scale up computational capabilities while improving their energy expenditure and speed. This work provides an orientation map for those interested in the vast topic of memristive systems with application to neuromorphic computing. We address the description of the most notable emerging devices and we illustrate models that capture the complex dynamical behavior of these systems under the dynamical-systems framework developed by Chua. We then review the memristive behavior under the perspective of statistical physics and percolation theory suited to describe fluctuations and disorder which are otherwise precluded in the dynamical-system approach. Percolation theory allows the investigation of these systems at the mesoscopic level, enabling material-independent modeling of non-linear conductance networks. We finally discuss recent and less recent successes in deep learning methods that bridge the field of physics-based and biological-inspired neuromorphic computing.

Neuromorphic engineering is rapidly developing as an approach to mimicking processes in brains using artificial memristors, devices that change conductivity in response to the electrical field (resistive switching effect). Memristor-based neuromorphic systems can overcome the existing problems of slow and energy-inefficient computing that conventional processors face. In the Introduction, the basic principles of memristor operation and its applications are given. The history of switching in sandwich structures and granular metals is reviewed in the Historical Overview. Particular attention is paid to the fundamental articles from the pre-memristor era (the 1960s-70s), which demonstrated the first evidence of resistive switching and predicted the filamentary mechanism of switching. Multi-dimensionality in neuromorphic systems: Despite the powerful computational abilities of traditional memristor arrays, they cannot repeat many organizational characteristics of biological neural networks, i.e., their multi-dimensionality. This part reviews the unconventional nanowire- and nanoparticle-based neuromorphic systems that demonstrate incredible potential for use in reservoir computing due to the unique spiking change in conductance similar to firing in neurons. Liquid-based neuromorphic devices: The transition of neuromorphic systems from solid to liquid state broadens the possibilities for mimicking biological processes. In this section, ionic current memristors are reviewed and, the working principles of which bring us closer to the mechanisms of information transmittance in real synapses. Nanofluids: A novel direction in neuromorphic engineering linked to the application of nanofluids for the formation of reconfigurable nanoparticle networks with memristive properties is given in this section. The Conclusion t summarizes the bullet points of the Review and provides an outlook on the future of liquid-state neuromorphic systems.