Connect with us

Chemistry

Harnessing Light: How Machine Learning Unlocks Superior Performance in Organic Crystals

Researchers have developed a machine learning workflow to optimize the output force of photo-actuated organic crystals. Using LASSO regression to identify key molecular substructures and Bayesian optimization for efficient sampling, they achieved a maximum blocking force of 37.0 mN — 73 times more efficient than conventional methods. These findings could help develop remote-controlled actuators for medical devices and robotics, supporting applications such as minimally invasive surgery and precision drug delivery.

Avatar photo

Published

on

Harnessing the power of light to unlock superior performance in organic crystals is an innovative breakthrough that can revolutionize various industries. Researchers at Waseda University have successfully leveraged machine learning techniques to enhance the output force of photo-actuated organic crystals, achieving a maximum blocking force of 37.0 mN – 73 times more efficient than conventional methods.

The performance of photomechanical crystals depends on factors such as molecular structures, crystal properties, and experimental conditions. Achieving high blocking forces remains challenging due to the complex interplay of these factors. To overcome this challenge, the researchers utilized two machine learning techniques: LASSO regression for molecular design and Bayesian optimization for selecting experimental conditions.

The first step led to a material pool of salicylideneamine derivatives, while the second enabled efficient sampling from this pool for real-world force measurements. As a result, the team successfully maximized the blocking force, achieving up to 3.7 times greater force output compared to previously reported values and accomplishing this at least 73 times more efficiently than conventional trial-and-error method.

This breakthrough has significant implications for remote-controlled actuators, small-scale robotics, medical devices, and energy-efficient systems. Photo-actuated crystals respond to light, enabling contactless and remote operation, making them ideal robotic components working in confined or sensitive environments. Their ability to generate force noninvasively with focused light could also be valuable for microsurgical tools and drug delivery mechanisms that require precise, remote actuation.

By leveraging a cleaner energy input – light irradiation – while maximizing mechanical output, these materials hold promise for eco-friendly manufacturing processes and devices aimed at reducing overall energy consumption. The proposed technology paves the way for more sophisticated, miniaturized devices, from wearable technology to aerospace engineering and remote environmental monitoring.

In conclusion, this study highlights the power of a machine learning-driven strategy in accelerating the development of high-performance photo-actuated materials, bringing them one step closer to real-world applications and commercial viability.

Air Quality

A Groundbreaking Approach to Soil Contamination Detection: Harnessing Machine Learning and Light-Based Imaging

A team of researchers has developed a new strategy for identifying hazardous pollutants in soil — even ones that have never been isolated or studied in a lab.

Avatar photo

Published

on

By

A team of researchers from Rice University and Baylor College of Medicine has developed an innovative strategy for identifying toxic compounds in soil, including those that have never been isolated or studied before. The new approach uses machine learning algorithms, theoretical predictions, and light-based imaging techniques to detect polycyclic aromatic hydrocarbons (PAHs) and their derivative compounds (PACs), which are linked to cancer and other serious health problems.

The researchers used surface-enhanced Raman spectroscopy, a light-based imaging technique that analyzes how light interacts with molecules, tracking the unique patterns or spectra they emit. These spectra serve as “chemical fingerprints” for each compound. To refine this method, the team designed signature nanoshells to enhance relevant traits in the spectra.

Using density functional theory, a computational modeling technique, the researchers calculated the spectra of a range of PAHs and PACs based on their molecular structure, generating a virtual library of “fingerprints.” Two complementary machine learning algorithms – characteristic peak extraction and characteristic peak similarity – were then used to parse relevant spectral traits in real-world soil samples and match them to compounds mapped out in the virtual library.

This method addresses a critical gap in environmental monitoring, opening the door to identifying a broader range of hazardous compounds, including those that have changed over time. The researchers tested this approach on soil from a restored watershed and natural area using artificially contaminated samples and a control sample, with results showing the new method reliably picked out even minute traces of PAHs.

The future holds promise for on-site field testing by integrating machine learning algorithms and theoretical spectral libraries with portable Raman devices into mobile systems. This would enable farmers, communities, and environmental agencies to test soil for hazardous compounds without needing to send samples to specialized labs and wait days for results.

Continue Reading

Chemistry

A Single Step Forward: Revolutionizing Drug Discovery with Carbon Insertion

A research team has pioneered a groundbreaking method that could accelerate drug discovery and reduce pharmaceutical development costs. Their work introduces a safe, sustainable way to insert a single carbon atom into drug molecules at room temperature.

Avatar photo

Published

on

The discovery of new medicines is an intricate process that requires patience, precision, and creativity. A research team from the University of Oklahoma has made a groundbreaking breakthrough that could accelerate this process, making it faster, safer, and more cost-effective. Their innovative method allows for the insertion of a single carbon atom into drug molecules at room temperature, opening up new possibilities for chemical diversity without compromising sensitive structures.

Nitrogen atoms and nitrogen-containing rings, known as heterocycles, play a crucial role in medicine development. A team led by OU Presidential Professor Indrajeet Sharma has found a way to modify these rings by adding just one carbon atom using a fast-reacting chemical called sulfenylcarbene. This process, called skeletal editing, transforms existing molecules into new drug candidates.

The significance of this discovery lies in its potential to change the molecule’s biological and pharmacological properties without altering its functionalities. This could unlock uncharted regions of chemical space in drug discovery, making it easier to find effective treatments for various diseases.

Unlike previous studies that relied on potentially explosive reagents and posed significant safety concerns, Sharma’s team has developed a bench-stable reagent that generates sulfenylcarbenes under metal-free conditions at room temperature. This achievement reduces environmental and health risks associated with metal-based carbenes.

The researchers are also exploring how this chemistry could revolutionize DNA-encoded library (DEL) technology, which allows for the rapid screening of billions of small molecules for their potential to bind to disease-relevant proteins. The metal-free, room-temperature conditions of the team’s new carbon insertion strategy make it a compelling candidate for use in DEL platforms.

By enabling precise skeletal editing in collaboration with the Damian Young group at the Baylor College of Medicine, Sharma’s approach could significantly enhance the chemical diversity and biological relevance of DEL libraries. This is particularly important as these are two key bottlenecks in drug discovery.

The cost of many drugs depends on the number of steps involved in making them. Adding a carbon atom in the late stages of development can make new drugs cheaper, akin to renovating a building rather than building it from scratch. By making these drugs easier to produce at large scale, we could reduce the cost of healthcare for populations around the world.

In conclusion, Sharma’s team has pioneered a groundbreaking method that accelerates drug discovery and reduces pharmaceutical development costs. Their innovative approach has far-reaching implications for the field of medicine, making it faster, safer, and more cost-effective.

Continue Reading

Chemistry

“Nature’s Filter: Plant-Based Extracts Show Promise in Removing Microplastics from Water”

The substances behind the slimy strings from okra and the gel from fenugreek seeds could trap microplastics better than a commonly used synthetic polymer. Previously, researchers proposed using these sticky natural polymers to clean up water. Now, they report that okra and/or fenugreek extracts attracted and removed up to 90% of microplastics in ocean water, freshwater and groundwater.

Avatar photo

Published

on

The fight against microplastic pollution has taken a promising turn. Researchers have discovered that extracts from plants like okra and fenugreek can trap and remove up to 90% of these tiny plastic particles from various types of water – ocean, freshwater, and groundwater. This breakthrough, published in ACS Omega, offers a biodegradable and non-toxic alternative to synthetic polymers currently used for wastewater treatment.

Researchers led by Rajani Srinivasan have been exploring plant-based approaches to clean contaminated water. In lab experiments, they found that extracts from okra, fenugreek, and tamarind formed sticky natural polymers that clump together with microplastics, making it easy to separate them from the water. The team demonstrated successful removals in freshwater and ocean water at a meeting of the American Chemical Society.

To extract these sticky plant polymers, researchers soaked sliced okra pods and blended fenugreek seeds in water overnight. They then removed the dissolved extracts, dried them into powders, and analyzed their composition. Initial tests showed that the powdered extracts contained polysaccharides, natural polymers capable of attracting microplastics.

The researchers then tested these plant extracts on real-world samples from waterbodies around Texas. The results varied depending on the original water source: okra worked best in ocean water (80%), fenugreek in groundwater (80-90%), and a combination of both in freshwater (77%). The team hypothesizes that this difference is due to the varying types, sizes, and shapes of microplastics present in each water sample.

Currently, polyacrylamide is used for contaminant removal during wastewater treatment. However, the researchers propose using okra and fenugreek extracts as biodegradable and non-toxic alternatives.

“Utilizing these plant-based extracts in water treatment will remove microplastics and other pollutants without introducing additional toxic substances to the treated water,” says Srinivasan. “This can significantly reduce long-term health risks to the population.”

The researchers acknowledge funding from various institutions, including the U.S. Department of Energy, Tarleton State University, and the National Science Foundation Research Experiences for Undergraduates program.

Continue Reading

Trending