Recent Patents on Nanotechnology - Online First

Over the past decades, biosensor technologies have experienced significant advances with the rapid development of novel nanomaterials and nanotechnologies. The analysis was performed using a patent dataset of nanobiosensors, including 2709 patent documents. The number of patents has been growing rapidly since 2000. Currently, China and the USA are the main contributors to the number of patents. Based on patent data, the most commonly used nanomaterials in biosensors are primarily metal-based, polymer-based, and carbon-based nanomaterials. Recently, the HCPs (highly cited patents, cited≥14) of biosensors include more than 10 types of nanomaterials, such as Ag NPs, Au NPs, graphene, CNTs, and polymer nanomaterials, indicating the diversity of nanomaterials and nanotechnologies used in biosensors. The number of HCPs from the USA is as high as 147, which is 3.5 times that of China. The use of nanomaterials in biosensors has been attracting increasing attention from researchers for decades. The sharp increase in patents since 2017 can be attributed to a significant number of new patents from India and China. In terms of the proportion of HCPs in the patent dataset, patented technologies from the US showed higher quality and value compared to those from other countries such as China, South Korea, and India. In the future, research on nanomaterials for biosensors, including metal and carbon-based nanomaterials, may focus on optimising their properties through the development of composite nanomaterials. With the application research of nucleic acid nanomaterials and MOFs, there could potentially be new technological breakthroughs in biosensors.

The recent rapid progress in artificial intelligence (AI) and the processing of big data imposes a strong demand to explore novel approaches for robust and efficient hardware solutions. Neuromorphic engineering and brain-inspired electronics take inspiration from biological information pathways in neural assemblies, particularly their fundamental building blocks and organizational principles. While resistive switching in memristive devices being widely considered as electronic synapse with potential applications for in-memory computing and vector matrix multiplication, further aspects of brain-inspired electronics require to explore both, organization principles from individual building units towards connected networks, as well as the resistive switching properties of each unit. In this context, nanogranular matter made of nano-objects, such as nanoparticles or nanowires, has gained considerable research interest due to emergent brain-like, scale-free switching dynamics originating from the self-organization of its building units into connected networks. In this study, we review resistive switching in nanogranular matter featuring metal nanoparticles as their functional building blocks. First, common deposition strategies for nanoparticles, as well as nanoparticle-based nanocomposites, are discussed, and challenges in the investigation of their inherited resistive switching properties are addressed. Secondly, an overview of resistive switching properties in nanogranular matter, ranging from individual nanoparticles over sparse nanoparticle arrangements to highly interconnected nanogranular networks, is provided. Thirdly, concepts and examples of information processing using nanoparticle networks are outlined. Finally, a survey on patents related to resistive switching in metal nanoparticles and nanocomposites is presented.

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.