Microwell microfluidics has been utilized for single-cell analysis to reveal heterogeneity in gene expression, signaling pathways, and phenotypic responses for identifying rare cell types, understanding disease progression, and developing more precise therapeutic strategies. However, designing microwell microfluidics is a considerably complex task, requiring knowledge, experience, and CAD software, as well as manual intervention, which often fails initial designs, demanding multiple costly and time-consuming iterations. In this study, we establish an autonomous large language model (LLM)-driven microwell design framework to generate code-based computer-aided design (CAD) scripts, that enables the rapid and reproducible creation of microwells with diverse geometries and imaging-based analysis. We propose a multimodal large language model (MLLM)-logistic regression framework based on integrating high-level semantic descriptions generated by MLLMs with image embeddings for image classification tasks, aiming to identify microwell occupancy and microwell shape. The fused multimodal representation is input to a logistic regression model, which is both interpretable and computationally efficient. We achieved significant improvements, exceeding 0.92 for occupancy classification and 0.99 for shape classification, across all evaluated MLLMs, compared with 0.50 and 0.55, respectively, when relying solely on direct classification. The MLLM-logistic regression framework is a scalable, efficient solution for high-throughput microwell image analysis. Our study demonstrates an autonomous design microwell platform by translating natural language prompts into optimized device geometries, CAD scripts and image analysis, facilitating the development of next-generation digital discovery by integration of literature mining, autonomous design and experimental data analysis.

Heterogeneous materials, crucial in various engineering applications, exhibit complex multiscale behavior, which challenges the effectiveness of traditional computational methods. In this work, we introduce the Micromechanics Transformer ({\em Micrometer}), an artificial intelligence (AI) framework for predicting the mechanical response of heterogeneous materials, bridging the gap between advanced data-driven methods and complex solid mechanics problems. Trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, Micrometer can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of microstructures, material properties under any loading conditions and We demonstrate the accuracy and computational efficiency of Micrometer through applications in computational homogenization and multiscale modeling, where Micrometer achieves 1\% error in predicting macroscale stress fields while reducing computational time by up to two orders of magnitude compared to conventional numerical solvers. We further showcase the adaptability of the proposed model through transfer learning experiments on new materials with limited data, highlighting its potential to tackle diverse scenarios in mechanical analysis of solid materials. Our work represents a significant step towards AI-driven innovation in computational solid mechanics, addressing the limitations of traditional numerical methods and paving the way for more efficient simulations of heterogeneous materials across various industrial applications.

Speed and precision are two major issues in robotics and in Industry of the Future (also known as Industry 4.0). Within this framework, RoMoCo research team of AS2M department at FEMTO-ST Institute has developed MiGriBot, a miniature robot able to perform 720 pick-and-place operations of sub-millimeter objects per minute. The results of this research work have been published in Science Robotics. These performances are made possible thanks to its architecture, that allows it to grip and manipulate micro-objects barely visible to the naked eye (from 40 micrometers to several hundred micrometers). In fact, where other microrobots have a rigid end-effector, MiGriBot is based on a principle with an articulated end.

Despite its wide range of uses, there are some problems with traditional atomic force microscopy (AFM) techniques. A high level of skill set and human interference is required for this procedure and is very time-consuming. In this article, the use of artificial intelligence (AI) in AFM and its combined benefits has been described. Atomic Force Microscopy (AFM) is a potent technology that permits the imaging of practically any surface along with polymers, ceramics, composites, glass, and biological materials. It is a surface scanning technique that has sub-nanometer scale resolution.

MIT researchers have pioneered a new fabrication technique that enables them to produce low-voltage, power-dense, high endurance soft actuators for an aerial microrobot. When it comes to robots, bigger isn't always better. Someday, a swarm of insect-sized robots might pollinate a field of crops or search for survivors amid the rubble of a collapsed building. MIT researchers have demonstrated diminutive drones that can zip around with bug-like agility and resilience, which could eventually perform these tasks. The soft actuators that propel these microrobots are very durable, but they require much higher voltages than similarly-sized rigid actuators.

What's happening in Japan is written all over our faces -- our blank, expressionless, masked faces. Never before, it seems safe to say, have so many people gone about masked. Thus we confront the microbes that assault us. "As self-protection, your mask is practically useless," says Shukan Gendai magazine this month. Commercial face masks, medical authorities say, can block particles measuring 3 to 5 micrometers.

Scientists have taken the tiny house trend to a whole new level. Using a new nanorobotic system, French scientists built a'microhouse' on top of an optical fiber that's as thin as human hair, which is 75 microns thick. It measures just 20 micrometers across but has several stunningly accurate details, including a front door, windows and even a tiled roof. A team of French scientists from the Femto-ST Institute built a 20-micrometer wide'microhouse' (pictured) on top of an optical fiber to demonstrate a new nanorobotic system A team of French scientists from the Femto-ST Institute detailed the process of creating the microhouse in new study published Friday in the Journal of Vacuum Science & Technology A. The new nanorobotic system, called μRobotex, uses a combination of technologies, including a tiny maneuverable robot, a focused ion beam and a gas injection device. To construct the microhouse, the scientists used a mix of origami and nanometer-precise robotics.

With invasive surgery sometimes being a literal pain in the ass, it's no surprise that scientists are working tirelessly to minimize the need for such procedures. Now, however, China's Harbin Institute of Techonology is hoping to bypass fiddly surgery completely, thanks to its new tiny, swimming robots. Inspired by the fastest human method of swimming, the front crawl, these nanobots travel in a similar fashion, with their magnetic arms rotating and propelling them forward as the researchers apply a magnetic field to the bot's arms. This cleverly designed bot is pretty swift too, able to swim the front crawl at an impressive 10 micrometers per second. Thanks to its hefty arms and impressive speed, the bots have a momentum strong enough that they can even pass through thick liquids like blood in order to administer medicine from inside your veins.

When your fancy high-tech tools aren't suited for the job, it's time to call the tinkers. The Field Museum of Natural History had a certain famous Tyrannosaurus rex skull they wanted examined with 3D imaging systems, but their dental scanners couldn't fit around the dinosaur's massive jaw. They contacted MIT Media Lab's Camera Culture group, which scanned the whole five-foot fossil with a $150 makeshift setup featuring a Microsoft Kinect. While the Kinect's resolution tops out at 500 micrometers, it only costs $100, compared to thousands of dollars for high-end 3D imaging systems that get down to 50 or 100 micrometers. Still, it was precise enough to scan the skull so scientists can look closer at mysterious holes in the jaw.

Along with it comes mm-wave antennas and greater challenges in testing. Gone are the days when antenna calibration for far-field characterization revolved around football-field-size installations and towers tens of meters tall. By the 1960s, antenna testing for near-field measurements moved indoors; those results could then be extrapolated to real-world far-field values. Properly testing today's antennas requires measurements at thousands of positions, each accurate to within one-hundredth of a wavelength. For signals at 183 gigahertz (the emission line for atmospheric water vapor absorption), which have a wavelength of 1,638 micrometers, the probe must be within 33 μm of its ideal position in every dimension on every measurement.