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Researchers from Princeton University, University of California Santa Barbara, University of Basel, and ETH Zurich have discovered new applications for nitrogen vacancy (NV) centre quantum sensors in condensed matter physics. These sensors, which offer nanoscale resolution across a wide range of temperatures, have been used to measure static magnetic fields in condensed matter systems.

NV centres can probe beyond average magnetic fields, enabling high precision noise sensing in diverse systems. They offer several advantages over other nanoscale probes, including the ability to probe both static and dynamic properties in a momentum and frequency-resolved way.

Condensed matter physics is a field that studies the physical properties of condensed phases of matter, such as solids and liquids. Recently, researchers from Princeton University, University of California Santa Barbara, University of Basel, and ETH Zurich have discovered new opportunities in this field for nanoscale quantum sensors, specifically nitrogen vacancy (NV) centre quantum sensors. These sensors offer unique advantages in studying condensed matter systems due to their quantitative, noninvasive, physically robust nature, and their ability to offer nanoscale resolution across a wide range of temperatures.

Advanced technologies enable the controlled release of medicine to specific cells in the body. Scientists argue these same technologies must be applied to agriculture if growers are to meet increasing global food demands.

Nanocarrier delivery has huge potential in agriculture; however, there are significant scientific and societal barriers to overcome. In this Review, the authors explore the state of the field, what lessons can be learned from nanomedicine, and discuss what scientific and societal issues need to be addressed.

(Nanowerk News) Advanced technologies enable the controlled release of medicine to specific cells in the body. Scientists argue these same technologies must be applied to agriculture if growers are to meet increasing global food demands.

In a new Nature Nanotechnology journal review paper (“Towards realizing nano-enabled precision delivery in plants”), scientists from UC Riverside and Carnegie Mellon University highlight some of the best-known strategies for improving agriculture with nanotechnology.

Chemists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory, Stony Brook University (SBU), and their collaborators have uncovered new details of the reversible assembly and disassembly of a platinum catalyst. The new understanding may offer clues to the catalyst’s stability and recyclability.

The work, described in a paper published in the journal Nanoscale (“Unravelling the origin of reaction-driven aggregation and fragmentation of atomically dispersed Pt catalyst on ceria support”), reveals how single platinum atoms on a cerium oxide support aggregate under reaction conditions to form active catalytic nanoparticles — and then, surprisingly, fragment once the reaction is stopped.

Fragmentation may sound shattering, but the scientists say it could be a plus.

A collaborative study by researchers at Lancaster and Radboud universities has pioneered a method to generate and control spin waves at the nanoscale, offering a new, energy-efficient approach to quantum computing.

Researchers at Lancaster University and Radboud University Nijmegen have successfully produced propagating spin waves on the nanoscale, unveiling a new method to modulate and amplify these waves.

Their discovery, published in Nature, could pave the way for the development of dissipation-free quantum information technologies. As the spin waves do not involve electric currents these chips will be free from associated losses of energy.

An Israeli startup has developed a way to make lead acid batteries last four times longer, disrupting a multi-billion-dollar industry and potentially making them the rechargeable – and recyclable – energy storage method of choice around the world.

Lead acid is the second most common battery technology worldwide and the power cells are currently used as the starter batteries in cars, trucks and motorcycles.

The batteries have a positive plate made of lead dioxide on one end, and a spongy lead negative plate on the other end, with sulfuric acid flowing between them both to conduct the electricity.

Nanoscale materials offer remarkable chemical and physical properties that transform theoretical applications, like single-molecule sensing and minimally invasive photothermal therapy, into practical realities.

The unparalleled features of nanoparticles make them promising for various research and industrial uses. However, effectively using these materials is challenging due to the absence of a rapid and consistent method to transfer a uniform monolayer of nanoparticles, a crucial step in device manufacturing.

One potential solution to this challenge lies in electrostatic assembly processes, where oppositely charged nanoparticles adhere to a surface, forming a monolayer that repels other similarly charged particles from attaching further. While effective, this process is often slow. Nature provides an innovative model to address this limitation through underwater adhesion strategies, which have evolved to circumvent similar problems.

Conductive aerogels have gained significant research interests due to their ultralight characteristics, adjustable mechanical properties, and outstanding electrical performance^1,2,3,4,5,6. These attributes make them desirable for a range of applications, spanning from pressure sensors^7,8,9,10 to electromagnetic interference shielding^11,12,13, thermal insulation^14,15,16, and wearable heaters^17,18,19. Conventional methods for the fabrication of conductive aerogels involve the preparation of aqueous mixtures of various building blocks, followed by a freeze-drying process^20,21,22,23. Key building blocks include conductive nanomaterials like carbon nanotubes, graphene, Ti₃C₂T_x MXene nanosheets^{24,25,26,27,28,29,30}, functional fillers like cellulose nanofibers (CNFs), silk nanofibrils, and chitosan^{29,31,32,33,34}, polymeric binders like gelatin^25,26, and crosslinking agents that include glutaraldehyde (GA) and metal ions^30,35,36,37. By adjusting the proportions of these building blocks, one can fine-tune the end properties of the conductive aerogels, such as electrical conductivities and compression resilience^38,39,40,41. However, the correlations between compositions, structures, and properties within conductive aerogels are complex and remain largely unexplored^{42,43,44,45,46,47}. Therefore, to produce a conductive aerogel with user-designated mechanical and electrical properties, labor-intensive and iterative optimization experiments are often required to identify the optimal set of fabrication parameters. Creating a predictive model that can automatically recommend the ideal parameter set for a conductive aerogel with programmable properties would greatly expedite the development process⁴⁸.

Machine learning (ML) is a subset of artificial intelligence (AI) that builds models for predictions or recommendations^49,50,51. AI/ML methodologies serve as an effective toolbox to unravel intricate correlations within the parameter space with multiple degrees of freedom (DOFs)^50,52,53. The AI/ML adoption in materials science research has surged, particularly in the fields with available simulation programs and high-throughput analytical tools that generate vast amounts of data in shared and open databases⁵⁴, including gene editing^55,56, battery electrolyte optimization^57,58, and catalyst discovery^59,60. However, building a prediction model for conductive aerogels encounters significant challenges, primarily due to the lack of high-quality data points. One major root cause is the lack of standardized fabrication protocols for conductive aerogels, and different research laboratories adopt various building blocks^35,40,46. Additionally, recent studies on conductive aerogels focus on optimizing a single property, such as electrical conductivity or compressive strength, and the complex correlations between these attributes are often neglected to understand^{37,42,61,62,63,64}. Moreover, as the fabrication of conductive aerogels is labor-intensive and time-consuming, the acquisition rate of training data points is highly limited, posing difficulties in constructing an accurate prediction model capable of predicting multiple characteristics.

Herein, we developed an integrated platform that combines the capabilities of collaborative robots with AI/ML predictions to accelerate the design of conductive aerogels with programmable mechanical and electrical properties (see Supplementary Fig. 1 for the robot–human teaming workflow). Based on specific property requirements, the robots/ML-integrated platform was able to automatically suggest a tailored parameter set for the fabrication of conductive aerogels, without the need for conducting iterative optimization experiments. To produce various conductive aerogels, four building blocks were selected, including MXene nanosheets, CNFs, gelatin, and GA crosslinker (see Supplementary Note 1 and Supplementary Fig. 2 for the selection rationale and model expansion strategy). Initially, an automated pipetting robot (i.e., OT-2 robot) was operated to prepare 264 mixtures with varying MXene/CNF/gelatin ratios and mixture loadings (i.e.