Lifeboat Foundation

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.

Researchers based at the Dept of Biology and School of Physics, Engineering and Technology have developed a remarkable new technology which is able to study single biological molecules using intrinsic twist properties to bring about essential functions in cells.

“Nano twists” that drive life

There are myriad so-called “chiral” molecules in biology, which have a fascinating property of not appearing to have the same structure were you to look at their image in a mirror — one of the best known examples being DNA, the “molecule of life”, whose chirality comes from its amazing double helix structure. This chirality, which looks in the case of extended DNA molecules like “nano twists”, results in a property which physicists describe as “symmetry breaking” which in turn can drive molecules into a range of different states. With input from sources of energy, these molecules can then jump between different states as part of their normal function, and it is this state jumping which essentially drives all processes in living cells — so chirality is an enormously fundamental feature which in effect effect steers key cellular processes.

Prof. Sergey Prosandeev and Prof. Bellaiche (who proposed with other co-workers the polar vortex ordering theoretically 20 years ago), joined this collaboration and further proved that the vortex distribution results obtained from experiments are consistent with theoretical calculations.

By controlling the number and orientation of these polarization distributions, it is expected that this can be utilized in a next-generation high-density memory device that can store more than 10,000 times the amount of information in the same-sized device compared to existing ones.

Dr. Yang, who led the research, explained the significance of the results, “This result suggests that controlling the size and shape of ferroelectrics alone, without needing to tune the substrate or surrounding environmental effects such as epitaxial strain, can manipulate ferroelectric vortices or other topological orderings at the nano-scale. Further research could then be applied to the development of next-generation ultra-high-density memory.”

Imaging the hot turbulence of aircraft propulsion systems may now be possible with sturdy sheets of composite materials that twist light beams, according to research led by the University of Michigan and Air Force Research Laboratory.

Researchers from Lancaster University and Radboud University Nijmegen have managed to generate propagating spin waves at the nanoscale and discovered a novel pathway to modulate and amplify them.

Recently, a team of chemists, mathematicians, physicists and nano-engineers at the University of Twente in the Netherlands developed a device to control the emission of photons with unprecedented precision. This technology could lead to more efficient miniature light sources, sensitive sensors, and stable quantum bits for quantum computing.

In a surprise discovery, Flinders University nanotechnology researchers have produced a range of different types of gold nanoparticles by adjusting water flow in the novel vortex fluidic device—without the need for toxic chemicals. The article, “Nanogold Foundry Involving High-Shear-Mediated Photocontact Electrification in Water,” has been published in Small Science.

“Batteries are the crux of many of the most important emerging technologies in both the civilian world and, important to our profession, on the battlefield,” said United States Military Academy Cadet Michael Williams. “More energy dense batteries allow, for instance, greater range on electric vehicles, longer battery lives for radios, and longer flight times for drones. Our work helps make manufacturing these batteries easier.”

Cadets Michael Williams, Avery Patel, and Nancy Astable have been working on a long-term project with their faculty mentors Dr. Enoch Nagelli, Dr. Simuck Yuk, and Army Col. John Burpo to develop new ways to maximize energy storage and generation for the U.S. Army Combat Capabilities Development Command’s Armaments Center. In collaboration with Cornell University, the team at USMA’s Department of Chemistry and Life Sciences is pursuing innovative approaches to increasing the quality and use of batteries and fuel cells.

The value of conducting scientific research to solve real-world problems is clear to the cadets.

Researchers at Rensselaer Polytechnic Institute have fabricated a device no wider than a human hair that will help physicists investigate the fundamental nature of matter and light. Their findings, published in the journal Nature Nanotechnology (“Topological valley Hall polariton condensation”), could also support the development of more efficient lasers, which are used in fields ranging from medicine to manufacturing.

The device is made of a special kind of material called a photonic topological insulator. A photonic topological insulator can guide photons, the wave-like particles that make up light, to interfaces specifically designed within the material while also preventing these particles from scattering through the material itself.

Because of this property, topological insulators can make many photons coherently act like one photon. The devices can also be used as topological “quantum simulators,” miniature laboratories where researchers can study quantum phenomenon, the physical laws that govern matter at very small scales.