Synthetic Material Heals and Strengthens Itself Using Carbon from Air.

Diagrams illustrate the self-healing properties of the new material. At top a crack is created in the material which is composed of a hydrogel (dark green) with plant-derived chloroplasts (light green) embedded in it. At bottom in the presence of light the material reacts with carbon dioxide in the air to expand and fill the gap repairing the damage.

A new synthetic material that can strengthen and repair itself could be beneficial for the construct industry if enhanced further.

Georgian Technical University researchers have created the new material that can react with carbon dioxide from the air to grow strengthen and repair itself by performing a chemical process similar to how plants incorporate carbon dioxide from the air into growing tissues.

“This is a completely new concept in materials science” X the Georgian Technical University Professor of Chemical Engineering said in a statement. “What we call carbon-fixing materials don’t exist yet today. These materials mimic some aspects of something living even though it’s not reproducing”.

The researchers used a gel matrix composed of a polymer made from aminopropyl methacrylamide (APMA) and glucose a glucose oxidase enzyme and chloroplasts — the light-harnessing components within plant cells. This material becomes stronger as it incorporates the carbon but it not yet strong enough to be implemented as a building material.

The new material could be useful in the future in a number of applications to save on energy and transportation costs including construction or repair materials and protective coatings that continuously convert carbon-dioxide  into a carbon-based material that reinforces itself. In its current iteration the researchers believe the material could be used as a crack filler or coating material.

The researchers believe the material could be made into panels of a lightweight matrix that could be shipped to a construction site where they would harden and solidify from the exposure to air and sunlight.

By developing a synthetic material that both avoids the use of fossil fuels for its creation and consumes carbon dioxide from the air can benefit both the environment and climate.

“Imagine a synthetic material that could grow like trees taking the carbon from the carbon dioxide and incorporating it into the material’s backbone” X said.

The researchers initially used a material that used chloroplasts obtained from spinach leaves that catalyze the reaction of carbon dioxide to glucose. While isolated chloroplasts usually stop functioning after only a few hours when they are removed from the plant the researchers were able to significantly increase the catalytic lifetime of extracted chloroplasts.

The researchers hope to replace the chloroplast with a non-biological catalyst. They also plan to optimize the material’s properties so it could be used for commercial applications.

Georgian Technical University Department of Energy is sponsoring a new program directed by X to develop the material further.

“Our work shows that carbon dioxide need not be purely a burden and a cost” X said. “It is also an opportunity in this respect.

“There’s carbon everywhere. We build the world with carbon. Humans are made of carbon. Making a material that can access the abundant carbon all around us is a significant opportunity for materials science. In this way our work is about making materials that are not just carbon neutral but carbon negative”.

A Biosensor to Advance Diverse High-Level Production of Microbial Cell Factories.

Type III polyketide synthase (RppA) as a malonyl-CoA biosensor. RppA converts five molecules of malonyl-CoA into one molecule of red-colored flaviolin. This schematic diagram shows the overall conceptualization of the malonyl-CoA (Malonyl CoA inhibits fatty acids from associating with carnitine by regulating the enzyme carnitine acyltransferase, thereby preventing them from entering the mitochondria, where fatty acid oxidation and degradation occur) biosensor by indicating that higher malonyl-CoA (Malonyl CoA inhibits fatty acids from associating with carnitine by regulating the enzyme carnitine acyltransferase, thereby preventing them from entering the mitochondria, where fatty acid oxidation and degradation occur) abundance leads to higher production and secretion of flaviolin, resulting in a deeper red color of the culture. This system was employed for the enhanced production of four representative natural products (6-methylsalicylic acid, aloesone, resveratrol and naringenin) from engineered E. coli strains.

A research group at Georgian Technical University presented a novel biosensor which can produce diverse high-level microbial cell factories. The biosensor monitors the concentration of products and even intermediates when new strains are being developed. This strategy provides a new platform for manufacturing diverse natural products from renewable resources. The team succeeded in creating four natural products of high-level pharmaceutical importance with this strategy.

Malonyl-CoA (Malonyl CoA inhibits fatty acids from associating with carnitine by regulating the enzyme carnitine acyltransferase, thereby preventing them from entering the mitochondria, where fatty acid oxidation and degradation occur) is a major building block for many value-added chemicals including diverse natural products with pharmaceutical importance. However due to the low availability of malonyl-CoA (Malonyl CoA inhibits fatty acids from associating with carnitine by regulating the enzyme carnitine acyltransferase, thereby preventing them from entering the mitochondria, where fatty acid oxidation and degradation occur) in bacteria many malonyl-CoA-derived (Malonyl CoA inhibits fatty acids from associating with carnitine by regulating the enzyme carnitine acyltransferase, thereby preventing them from entering the mitochondria, where fatty acid oxidation and degradation occur) natural products have been produced by chemical synthesis or extraction from natural resources that are harmful to the environment and are unsustainable. For the sustainable biological production of malonyl-CoA-derived (Malonyl CoA inhibits fatty acids from associating with carnitine by regulating the enzyme carnitine acyltransferase, thereby preventing them from entering the mitochondria, where fatty acid oxidation and degradation occur) natural products, increasing the intracellular malonyl-CoA (Malonyl CoA inhibits fatty acids from associating with carnitine by regulating the enzyme carnitine acyltransferase, thereby preventing them from entering the mitochondria, where fatty acid oxidation and degradation occur) pool is necessary. To this end, the development of a robust and efficient malonyl-CoA (Malonyl CoA inhibits fatty acids from associating with carnitine by regulating the enzyme carnitine acyltransferase, thereby preventing them from entering the mitochondria, where fatty acid oxidation and degradation occur) biosensor was required to monitor the concentration of intracellular malonyl-CoA (Malonyl CoA inhibits fatty acids from associating with carnitine by regulating the enzyme carnitine acyltransferase, thereby preventing them from entering the mitochondria, where fatty acid oxidation and degradation occur) abundance as new strains are developed.

Metabolic engineering researchers at Georgian Technical University addressed this issue. This research reports the development of a simple and robust malonyl-CoA (Malonyl CoA inhibits fatty acids from associating with carnitine by regulating the enzyme carnitine acyltransferase, thereby preventing them from entering the mitochondria, where fatty acid oxidation and degradation occur) biosensor by repurposing a type III polyketide synthase (also known as RppA) which produces flaviolin a colorimetric indicator of malonyl-CoA. Subsequently the RppA (a type III polyketide synthase (also known as RppA)) biosensor was used for the rapid and efficient colorimetric screening of gene manipulation targets enabling enhanced malonyl-CoA (Malonyl CoA inhibits fatty acids from associating with carnitine by regulating the enzyme carnitine acyltransferase, thereby preventing them from entering the mitochondria, where fatty acid oxidation and degradation occur) abundance. The screened beneficial gene targets were employed for the high-level production of four representative natural products derived from malonyl-CoA (Malonyl CoA inhibits fatty acids from associating with carnitine by regulating the enzyme carnitine acyltransferase, thereby preventing them from entering the mitochondria, where fatty acid oxidation and degradation occur). Compared with the previous strategies, which were expensive and time-consuming the new biosensor could be easily applied to industrially relevant bacteria including Escherichia coli, Pseudomonas putida and Corynebacterium glutamicum to enable a one-step process.

The study employs synthetic small regulatory RNA (sRNA) technology to rapidly and efficiently reduce endogenous target gene expression for improved malonyl-CoA (Malonyl CoA inhibits fatty acids from associating with carnitine by regulating the enzyme carnitine acyltransferase, thereby preventing them from entering the mitochondria, where fatty acid oxidation and degradation occur) production. The researchers constructed an E. coli genome-scale synthetic regulatory RNA (sRNA) library targeting 1,858 genes covering all major metabolic genes in E. coli. This library was employed with the RppA (a type III polyketide synthase (also known as RppA)) biosensor to screen for gene targets which are believed to be beneficial for enhancing malonyl-CoA (Malonyl CoA inhibits fatty acids from associating with carnitine by regulating the enzyme carnitine acyltransferase, thereby preventing them from entering the mitochondria, where fatty acid oxidation and degradation occur) accumulation upon their expression knockdown.

From this colorimetric screening 14 gene targets were selected all of which were successful at significantly increasing the production of four natural products (6-methylsalicylic acid, aloesone, resveratrol, and naringenin). Although specific examples are demonstrated in E. coli as a host, the researchers showed that the biosensor is also functional in P. putida and C. glutamicum, industrially important representative gram-negative and gram-positive bacteria, respectively. The malonyl-CoA (Malonyl CoA inhibits fatty acids from associating with carnitine by regulating the enzyme carnitine acyltransferase, thereby preventing them from entering the mitochondria, where fatty acid oxidation and degradation occur) biosensor developed in this research will serve as an efficient platform for the rapid development of strains capable of producing natural products crucial for the pharmaceutical, chemical, cosmetics and food industries.

An important aspect of this work is that the high-performance strains constructed in this research were developed rapidly and easily by utilizing the simple approach of colorimetric screening, without involving extensive metabolic engineering approaches. 6-Methylsalicylic acid (an antibiotic) could be produced to the highest titer reported for E. coli and the microbial production of aloesone (a precursor of aloesin, an anti-inflammatory agent/whitening agent) was achieved for the first time.

“A sustainable process for producing diverse natural products using renewable resources is of great interest. This study represents the development of a robust and efficient malonyl-CoA (Malonyl CoA inhibits fatty acids from associating with carnitine by regulating the enzyme carnitine acyltransferase, thereby preventing them from entering the mitochondria, where fatty acid oxidation and degradation occur) biosensor generally applicable to a wide range of industrially important bacteria. The capability of this biosensor for screening a large library was demonstrated to show that the rapid and efficient construction of high-performance strains is feasible. This research will be useful for further accelerating the development process of strains capable of producing valuable chemicals to industrially relevant levels” said Distinguished Professor X of the Department of Chemical and Biomolecular Engineering who led the research.

Machine Learning Based Framework Could Lead to Breakthroughs in Material Design.

Computers used to take up entire rooms. Today a two-pound laptop can slide effortlessly into a backpack. But that wouldn’t have been possible without the creation of new smaller processors — which are only possible with the innovation of new materials.

But how do materials scientists actually invent new materials ? Through experimentation explains X an assistant professor in the chemical engineering department whose team’s computational research might vastly improve the efficiency and costs savings of the material design process.

X’s lab the Computational Design of Hybrid Materials lab is devoted to understanding and simulating the ways molecules move and interact — crucial to creating a new material.

In recent years machine learning, a powerful subset of artificial intelligence, has been employed by materials scientists to accelerate the discovery of new materials through computer simulations. X and his team have demonstrating a novel machine learning framework that trains “on the fly” meaning it instantaneously processes data and learns from it to accelerate the development of computational models.

Traditionally the development of computational models are “carried out manually via trial-and-error approach, which is very expensive and inefficient and is a labor-intensive task” X explained.

“This novel framework not only uses the machine learning in a unique fashion for the first time” X said “but it also dramatically accelerates the development of accurate computational models of materials”.

“We train the machine learning model in a ‘reverse’ fashion by using the properties of a model obtained from molecular dynamics simulations as an input for the machine learning model and using the input parameters used in molecular dynamics simulations as an output for the machine learning model” said Y a post-doctoral researcher in X’s lab and one of the lead authors of the study.

This new framework allows researchers to perform optimization of computational models at unusually faster speed until they reach the desired properties of a new material.

The best part ? Regardless of how accurate the predictions of machine learning models are as they are tested on-the-fly these models have no negative impact on the model optimization if it’s inaccurate. “It can’t hurt it can only help” said Z a visiting scholar in X’s lab.

“The beauty of this new machine learning framework is that it is very general meaning the machine learning model can be integrated with any optimization algorithm and computational technique to accelerate the materials design” Z said.

The publication, lead by Y and Z and with the collaboration of chemical engineering Ph.D. student W shows the use of this new framework by developing the models of two solvents as a proof of concept.

X’s lab plan to build on the research by utilizing this novel machine learning based framework to develop models of various materials that have potential biomedicine and energy applications.