Recent Patents on Nanotechnology (Volume 20 - Issue 2)

Nanostructured Neuromorphic Devices: Progress Towards In materia Computing

2026-04-02

Advances in Neuromorphic Computing Devices: Insights on Both Conventional and Unconventional Architectures

2026-04-02

Neuromorphic circuits and devices have been introduced in the last decades as key elements for developing of new paradigms of computation, inspired by the intent to mimic elementary neuron structure and biological mechanisms, for the overcoming of energy and time-consumption bottlenecks encountered by digital computing (DC) technologies. Although the term “neuromorphic” is in common use, its meaning is often misunderstood and indistinctly associated with many different technologies, based on both conventional and unconventional electronic components and architectures. Here an overview of the different technological strategies used for developing neuromorphic computing systems is proposed, with an insight on the neuromorphic features they implement and a special focus on the technological strategies and patents that exploit unconventional computing paradigms.

Introduction to Memristive Mechanisms and Models

2026-04-02

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 and are currently used in patents for the design of neuromorphic circuits, 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 nonlinear 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.

Resistive Switching in Nanoparticle-Based Nanocomposites

2026-04-02

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.

From Solid to Fluid: Novel Approaches in Neuromorphic Engineering

2026-04-02

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. Recent patents in non-conventional neuromorphic devices: This part is devoted to the most actual patents in the field of nanoparticle-based and liquid-state neuromorphic systems. The conclusion summarizes the bullet points of the Review and provides an outlook on the future of liquid-state neuromorphic systems.

Formulation Optimization and Evaluation of Patented Solid Lipid Nanoparticles of Ambrisentan for Pulmonary Arterial Hypertension

2026-04-02

Background: Ambrisentan is a new endothelin receptor antagonist extensively used to manage pulmonary or pulmonary arterial hypertension.

Objective: The therapeutic efficacy of Ambrisentan is limited due to its reduced solubility, higher log P (3.4), and thus less bioavailability. The recent investigation was concentrated on the improvement of solubility, and bioavailability of Ambrisentan for the therapy of hypertension via solid lipid nanoparticles (SLN) administered orally.

Methods: XRD evaluated the compatibility of Ambrisentan with lipids with FTIR, DSC, and crystalline nature. The SLN was developed by High-pressure homogenization method. The Glyceryl monostearate and Tween 80 indicated the highest solubility, hence selected. The optimization was performed with Box-Behnken Design considering the concentration of GMS (X1), Tween 80 (X2), stirring speed (X3) as independent factors and particle size (Y1), entrapment efficiency (Y2) as dependent factors. The Patents on the SLN are Indian 202321053691, U.S. Patent, 10,973,798B2, U.S. Patent 10,251,960B2, U.S. Patent 2021/0069121A1 and U.S. Patent 2022/0151945A1.

Results: The optimized batch F1 showed particle size (130 nm), ZP (-18.9 mV), and entrapment efficiency (85.73%). The dual release pattern (prompt and sustained) was achieved with the SLNloaded Ambrisentan for about 24 hours. The lyophilized sample was subjected to SEM, which also revealed a spherical shape of a colloidal dispersion with a particle size of 126 nm. Hence, the F1 batch is highly recommended for solid oral delivery and also for the pilot-plant scale-up.

Conclusion: A marked improvement in the solubility and dissolution of Ambrisentan was attained with the SLN. Moreover, the sustained delivery via the oral route enabled the patient's comfort, compliance, and therapeutic efficacy.

In-vivo Pharmacokinetic Assessment and In-vitro Characterization of Strategically Optimized Perphenazine-loaded Nanostructured Lipid Carriers for Nose-to-brain Targeting

2026-04-02

Background: Perphenazine (PPZ) is a prevalent antipsychotic medication used to treat schizophrenia. After oral treatment, however, it shows substantial first-pass metabolism and decreased bioavailability.

Objective: The goal of this research was to incorporate PPZ into nanostructured lipid carriers and thereby improve its bioavailability and brain targeting (PPZ-NLCs).

Methods: PPZ-NLCs were formulated by a high-pressure homogenization methodology under heated conditions and optimized by applying a 2³-full factorial design.

Results: The optimized PPZ-NLCs showed particle size 167.5 nm, PDI 0.277, Zeta Potential of -28.8 mV, and 98.6% EE. The drug release during In-vitro experiments of PPZ-NLCs exhibited a prolonged release profile of the drug best fitted into the Higuchi kinetic model. PPZ-NLCs when examined In-vivo pharmacokinetically a significant increase in t_1/2, AUC_0-∞, and Cmax was observed which indicates a greater bioavailability and a lesser elimination (Kel).

Conclusion: These results suggested the superiority of NLCs in enhancing the bioavailability of PPZ drug and their suitability for successful brain targeting, which could be subject to a patent application to protect the novel formulation.

Research on Controllable Synthesis and Growth Mechanism of Sodium Vanadium Fluorophosphate Nanosheets

2026-04-02

Background: Sodium vanadium fluorophosphate is a sodium ion superconductor material with high sodium ion mobility and excellent cyclic stability, making it a promising cathode material for sodium-ion batteries. However, most of the literature and patents report preparation through traditional methods, which involve complex processes, large particle sizes, and low electronic conductivity, thereby limiting development progress.

Objective: Aiming at the limitation of high cost and poor performance of vanadium sodium fluorophosphate cathode material, the low temperature and high-efficiency nano preparation technology was developed.

Methods: This study uses a homogenizer with high dispersion and shear force to directionally control the collision of sodium vanadium fluorophosphate nanoparticles with higher specific surface energy during the initial nucleation stage, forming nanosheet structures.

Results: The growth mechanism of these nanosheets was analyzed using SEM, XRD, AFM, and DFT simulation. Results indicate that the crystal surfaces with higher surface energy undergo directional collisions in the early nucleation stage, gradually reducing the surface energy and stabilizing the system, resulting in sodium vanadium fluorophosphate nanosheets.

Conclusion: Due to the larger specific surface area and pore structure, these nanosheets exhibit excellent rate performance and cycle stability, making them suitable for application and promotion in the field of fast-charging energy storage.

Patent Selections

2026-04-02