System Bits: July 10

Origami electronics; machine-learning for drug development, catalytic design.


Foldable electronic switches and sensors
Using inexpensive materials, UC Berkeley engineers have created a method to fabricate foldable electronic switches and sensors directly onto paper, along with prototype generators, supercapacitors and other electronic devices for what they said is a range of applications.

Besides the fact that it is readily available and low cost, the team pointed out that paper offers an intriguing potential: simply folding it could switch circuits on and off or otherwise change their activity in what could be considered a kind of electronic origami.

Most of the efforts to fabricate electrodes onto paper with sufficient conductivity for practical have used expensive metals such as gold or silver as the conducting material, thereby negating the potential savings of paper as a substrate.

However, the UC Berkeley team’s approach uses the inexpensive element molybdenum as the source of the conducting metal. Molybdenum is added to gelatin in solution, which binds to carbon in the gelatin. The paper is then coated with the solution and dried. Next, a laser beam precisely “writes” the desired circuitry patterns, heating the molybdenum to about 1,000 degrees centigrade, and forming conductors of durable molybdenum carbide.

Berkeley engineers used a laser to “write” the circuitry for an electronic switch onto paper. They showed that folding and unfolding the paper could turn the circuit on and off.
Source: UC Berkeley

Currently, the laser-written circuits are about 100 microns wide, and all of the unheated portions of the paper remain non-conductive. The gelatin coating provides the carbon for the conductive compound but also prevents the laser beam from burning the paper.

Liwei Lin, professor of mechanical engineering and senior author of a paper in the journal Advanced Materials reporting the versatile new technology said without the gelatin, the paper would turn into ashes.

The team envisages widespread potential for the new, disposable paper electronics. For example, circuitry to detect heavy metal contamination could be “written” on paper to economically monitor toxins.
A sensor made of several electrodes integrated onto a paper circuit could detect unsafe lead levels in a drop of water — or in a drop of a patient’s blood, said Xining Zang — lead author of the journal article, and now a postdoctoral scientist at MIT — who led the research as a Berkeley mechanical engineering graduate student in Lin’s lab.

“The electrodes would have small gaps between them, and the presence of heavy metal in the sample would complete the circuit,” she explained.
She added that a self-contained, disposable sensor could be very useful in developing countries where portable, storable and inexpensive public health tools are in particular demand.

Next in this week’s System Bits are two items on machine learning research.

Machine-learning model for speeding drug development
A team of MIT researchers report they have fully automated and improved the process to design new molecules for pharmaceutical development, which is primarily a manual, time-consuming process that’s prone to error.
The team expects the new process to drastically speed things up, as well as produce better results. They explained that drug discovery relies on lead optimization. In this process, chemists select a target (“lead”) molecule with known potential to combat a specific disease, then tweak its chemical properties for higher potency and other factors.

Often, the team explained, chemists use expert knowledge and conduct manual tweaking of molecules, adding and subtracting functional groups — atoms and bonds responsible for specific chemical reactions — one by one. Even if they use systems that predict optimal chemical properties, chemists still need to do each modification step themselves, which can take hours for each iteration, and may still not produce a valid drug candidate.

MIT researchers have developed a machine-learning model that better selects molecule candidates for therapeutics, while also allowing for automated modification of the molecular structure for higher potency. The innovation has potential to speed up drug development.
Source: MIT

Now, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Department of Electrical Engineering and Computer Science (EECS) have developed a model that better selects lead molecule candidates based on desired properties. It also modifies the molecular structure needed to achieve a higher potency, while ensuring the molecule is still chemically valid.

Basically, the model takes as input molecular structure data and directly creates molecular graphs — detailed representations of a molecular structure, with nodes representing atoms and edges representing bonds. It breaks those graphs down into smaller clusters of valid functional groups that it uses as “building blocks” that help it more accurately reconstruct and better modify molecules.

The team explained the motivation behind this was to replace the inefficient human modification process of designing molecules with automated iteration and assure the validity of the molecules generated.

Machine learning algorithm to improve catalytic design
Chemical engineers at Rice University and Pennsylvania State University have shown that combining machine learning and quantum chemistry can save time and expense in designing new catalysts.

Rice’s Thomas Senftle, co-author of a new study published online recently noted, “Large amounts of data are generated in computational catalysis, and the field is starting to realize that data science tools can be extremely valuable for sifting through high-volume data to look for fundamental correlations that we might otherwise miss. That’s what this paper was really about. We combined well-established tools for data generation and analysis in a way that allowed us to look for correlations we wouldn’t otherwise have noticed.”

Chemical engineers at Rice University and Penn State have combined machine learning and quantum chemistry to design new catalysts.
Source: Rice University

A catalyst is a substance that accelerates chemical reactions without being consumed by them. The catalytic converters in automobiles, for example, contain metals like platinum and palladium that aid in reactions that break down air pollutants. Catalysts are a mainstay of the chemical and pharmaceutical industries, and the global market for catalysts is estimated at $20 billion per year.

Read more about their process here.

Leave a Reply

(Note: This name will be displayed publicly)