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.

While wearable technologies with embedded sensors, such as smartwatches, are widely available, these devices can be uncomfortable, obtrusive and can inhibit the skin’s intrinsic sensations.

“If you want to accurately sense anything on a biological surface like skin or a leaf, the interface between the device and the surface is vital,” said Professor Yan Yan Shery Huang from Cambridge’s Department of Engineering, who led the research. “We also want bioelectronics that are completely imperceptible to the user, so they don’t in any way interfere with how the user interacts with the world, and we want them to be sustainable and low waste.”

Continue reading “Imperceptible sensors made from ‘electronic spider silk’ can be printed directly on human skin” »

Scientists use laser ablation technology to develop a deformable micro-supercapacitor. Professor Jin Kon Kim and Dr. Keon-Woo Kim from the Department of Chemical Engineering at Pohang University of Science and Technology (POSTECH), in collaboration with Dr. Chanwoo Yang and Researcher Seong Ju Park from the Korea Institute of Industrial Technology (KITECH), have achieved a significant breakthrough in developing a small-scale energy storage device capable of stretching, twisting, folding, and wrinkling. Their research has been published in the electronic engineering journal, npj Flexible Electronics.

The advent of wearable technology has brought with it a pressing need for energy storage solutions that can keep pace with the flexibility and stretchability of soft electronic devices.

Micro supercapacitors (MSCs) have emerged as a promising candidate for deformable energy storage, due to high-power density, rapid charging, and long cycle life.

After trying multimodal AI in the Meta Ray-Ban smart glasses, I was impressed.

What if changes in a person’s stress levels could be detected while they sleep using wearable devices? A new study by University of Vermont researchers published in PLOS Digital Health is the first to find changes in perceived stress levels reflected in sleep data—an important step towards identifying biomarkers that may help flag individuals in need of support.

Given how critical sleep is to physical and mental health, the research team suspected signals might exist in sleep data, says Laura Bloomfield, a research assistant professor of mathematics and statistics and lead author of the study. “Changes in stress are visible.”

When parsing baseline sleep data, the researchers found “consistent associations” between people’s perceived stress scores and factors such as total sleep time, resting heart rate and heart rate variability, and respiratory rate.

An in situ phase-separation method at microscale level creates stretchable superelastic porous nanocomposite conductors, reduces conductive filler usage and enables strain-resilient wireless wearable and implantable bioelectronics.

Traditional AUVs rely on thrusters or pumps to adjust depth, which consumes considerable energy and generates noise. Team BayMax’s design replaces this system with a BCD that employs reversible hydrogen fuel cells. By splitting water into hydrogen and oxygen gases, the BCD can precisely control the ROV’s buoyancy, mimicking the swim bladders found in fish.

This approach offers many advantages. “The cool thing about this for us is that it’s cutting-edge technology,” remarked Bare. We’re the first to implement it in a device with such comprehensive controls, making it truly groundbreaking.”

Continue reading “RICE made underwater robot swim with water-splitting fuel cells” »

I found this on NewsBreak: Scientists Invented a Bizarre New Material That Gets Tougher When You Hit It.

The Limitless Pendant is part of the whole Limitless system, which the company is launching today. (Oh, and in case you’re wondering: yes, it’s very much a reference to the movie.) Siroker’s last AI product, Rewind, was an app that ran on your computer and would record your screen and other data in order to help you remember every tab, every song, every meeting, everything you do on your computer. (When the company first teased the Limitless Pendant, it was actually called the Rewind Pendant.) Limitless has similar aims, but instead of just running on your computer, it’s meant to collect data in the cloud and the real world, too, and make it all available to you on any device. Rewind is still around, for the folks who want the all-local, one-computer approach — but Siroker says the cross-platform opportunity is much bigger.

“The core job to be done is initially around meetings,” Siroker tells me. “Preparing you for meetings, transcribing meetings, giving you real-time notes of meetings and summaries of meetings.” For $20 a month, the app will capture audio from your computer’s mic and speakers, and you can also give it access to your email and calendar. With that combination — and ultimately all the other apps you use for work, Siroker says — Limitless can do a lot to help you keep track of conversations. What was that new app someone mentioned in the board meeting? What restaurant did Shannon say we should go to next time? Where did I leave off with Jake when we met two weeks ago? In theory, Limitless can get that data and use AI models to get it back to you anytime you ask.

Siroker and I are talking the day after the first reviews of the Humane AI Pin came out, and he’s careful to differentiate his company’s approach from these all-encompassing AI tools. “We’re trying to do a few things exceptionally well, not be a mile wide and an inch deep,” he says. “We’re not, you know, trying to reinvent the wheel with lasers.” His plan is to integrate into all the apps you use and put Limitless inside of those apps; you should be able to take notes in Notion or get action items in Slack, he thinks, instead of having to go to some other app entirely. “Why would I even have to make you log into my cloud based app, when I could just have you show up to the thing you’re already using?”