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.

Synthetic biology has been game-changing and with generative artificial intelligence, generative biology holds immense potential; let’s just speed it up.

Episode Disclaimer — The views presented in this episode are those of the speaker and do not necessarily represent the views of the United States Department of Defense (DoD) or its components.

Dr. Diane DiEuliis, Ph.D. is a Distinguished Research Fellow at National Defense University (NDU — https://www.ndu.edu/), an institution of higher education, funded by the United States Department of Defense, aimed at facilitating high-level education, training, and professional development of national security leaders. Her research areas focus on emerging biological technologies, biodefense, and preparedness for biothreats. Specific topic areas under this broad research portfolio include dual-use life sciences research, synthetic biology, the U.S. bioeconomy, disaster recovery, and behavioral, cognitive, and social science as it relates to important aspects of deterrence. Dr. DiEuliis currently has several research grants in progress, and teaches in foundational professional military education.

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This video explores the 4th to the 10th dimensions of time. Watch this next video about the 10 stages of AI: • The 10 Stages of Artificial Intelligence.
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ESA’s newly graduated astronauts reach the end of one year of rigorous basic astronaut training. Discover the journey of Sophie Adenot, Rosemary Coogan, Pablo Álvarez Fernández, Raphaël Liégeois, Marco Sieber, and Australian Space Agency astronaut candidate Katherine Bennell-Pegg. Selected in November 2022, the group began their training in April 2023.

Basic astronaut training provides the candidates with an overall familiarisation and training in various areas, such as spacecraft systems, spacewalks, flight engineering, robotics and life support systems as well as survival and medical training. They received astronaut certification at ESA’s European Astronaut Centre on 22 April 2024.

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Researchers at the Francis Crick Institute, the National Cancer Institute (NCI) of the U.S. National Institutes of Health (NIH) and Aalborg University in Denmark have found that vitamin D encourages the growth of a type of gut bacteria in mice which improves immunity to cancer.

Reported today in Science, the researchers found that mice given a diet rich in vitamin D had better immune resistance to experimentally transplanted cancers and improved responses to immunotherapy treatment. This effect was also seen when gene editing was used to remove a protein that binds to vitamin D in the blood and keeps it away from tissues.

Surprisingly, the team found that vitamin D acts on epithelial cells in the intestine, which in turn increase the amount of a bacteria called Bacteroides fragilis. This microbe gave mice better immunity to cancer as the transplanted tumours didn’t grow as much, but the researchers are not yet sure how.

Structural and dynamic DNA nanosciences offer unique tools for engineering bottom–up synthetic cells. This Review provides a holistic overview for using DNA as a structural material, for designing functional entities, and for information-processing circuits for adaptive and interactive behaviour.

A trailblazer in the field of therapeutic genome editing, Fyodor D. Urnov’s research focuses on developing medicines for devastating genetic diseases.

Jason Comander, MD, PhD, performs the procedure to deliver the CRISPR-based medicine as part of the BRILLIANCE trial in September 2020 at Mass Eye and Ear. Credit: Mass Eye and Ear.

All 14 trial participants, including 12 adults (ages 17 to 63) and two children (ages 10 and 14), were born with a form of Leber Congenital Amaurosis (LCA) caused by mutations in the centrosomal protein 290 (CEP290) gene. They underwent a single injection of a CRISPR/Cas9 genome editing medicine, EDIT-101 in one eye via a specialized surgical procedure. This trial, which included the first patient to ever receive a CRISPR-based investigational medicine directly inside the body, focused primarily on safety with a secondary analysis for efficacy.

No serious treatment or procedure-related adverse events were reported, nor were there any dose-limiting toxicities. For efficacy, the researchers looked at four measures: best-corrected visual acuity (BCVA); dark-adapted full-field stimulus testing (FST), visual function navigation (VNC, as measured by a maze participants completed), and vision-related quality of life.