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Researcheres have developed a groundbreaking method for high-throughput mechanical exfoliation, enabling low-cost production of van der Waals nanosheets. The technique offers a significant advancement in the scalable fabrication of low-cost devices while maintaining excellent performance.
Researchers are advancing high-density memory storage with an innovative method to form block copolymer structures. By pushing boundaries with unique tetragonally packed inverted cylinders, these novel structures bring us one step closer to ultra-compact future memory devices.
Comprehensive analysis of nanocellulose as a promising component in the development of advanced flexible materials. Emphasizes potential applications in electronics, energy storage, and biomedicine, and investigates the challenges and future perspectives in the field.
Researchers have developed a low-cost and simple method for printing electronics using regular pens and a benchtop plotter. This technique allows for the use of virtually any solution-processable nanomaterial ink without the need for expensive equipment or ink optimization, making printed electronics more accessible.
Researchers report remarkable quantum composites based on polymers with fillers comprised of a van der Waals material that reveals multiple quantum condensate phases. The quantum composites revealed unusually high dielectric constant while remaining electrically insulating.
Researchers demonstrate a new way to create chiral nanostructures using a laser beam. By carefully controlling the direction of the laser beam's vibrations, the researchers created a variety of chiral structures made of platinum oxide in unique shapes, like twisted micropillars and spiral superstructures.
Hygromorphs are materials that can change their shape or size in response to changes in humidity or moisture. Synthetic hygromorphs, designed to have specific responses to changes in humidity, can be used in various applications, such as in sensors and actuators. A recent review generalizes a full design process for a 4D printed hygromorph which includes designing concepts, using additive manufacturing to produce designs, and measuring actuation.
Perovskite solar cells (PSCs) are complex devices made up of multiple materials that have many different factors affecting their properties, which makes it difficult to analyze them comprehensively. Machine Learning (ML) can efficiently handle these complexities and help scientists in the design of new PSCs. We outline the current state and future prospects of ML in perovskite solar cell research, including data sources, feature extraction, algorithms, model validation, interpretation, and challenges.