Researchers have developed a transparent temperature sensor capable of precisely and quickly measuring temperature changes caused by light. This technology is expected to contribute to the advancement of various applied bio devices that rely on sensitive temperature changes.
The photothermal effect using plasmonic nanomaterials has recently been widely proposed in various bio-application fields, such as brain nerve stimulation, drug delivery, cancer treatment, and ultra-high-speed PCR due to its unique heating properties using light. However, measuring temperature changes by photothermal phenomena still relies on an indirect and slow measurement method using a thermal imaging camera, leading to the limitation that it is not suitable for local temperature measurement at the level of a single cell, which changes rapidly at the level of several milliseconds to tens of micrometers.
Due to the absence of precise information on temperature changes, photothermal effect technology has raised concerns about the understanding of biological changes and stable clinical application resulting from precise temperature changes, despite the spreading effect of its application.
Accordingly, the joint research team, which included Professor Kang Hong-gi of the Department of Electrical Engineering and Computer Science at DGIST and Dr. Chung Seung-jun of the Soft Hybrid Materials Research Center at KIST, developed a temperature sensor technology that can measure even rapid temperature changes in less than a few milliseconds by using the thermoelectric effect, in which a voltage signal is generated by rapid charge transfer triggered by a difference in temperature.
In particular, the team established a direct photothermal phenomenon measurement technology with reduced interference by light utilizing an organic thermoelectric layer of transparent PEDOT:PSS, a conductive polymer suitable for storing charges.
The 50-nanometer thin PEDOT:PSS thermoelectric sensor secures high transparency at 97% on average in the visible light zone and can be directly applied to the area of photothermal phenomenon, minimizing light interference for various photothermal bioengineering and medical applications. In addition, since a low-temperature solution process could be used for the polymer thermoelectric material used, it was prepared using an inkjet printing process, which is simpler to manufacture than a general semiconductor process, with a high degree of design freedom thus giving it an advantage in the printing process.
The transparent thermoelectric temperature sensor technology developed through this study can be used to understand the mechanism of the optical neural interface for controlling brain activity using light, which has recently been known broadly through optogenetics. It is a key technology in that it can be utilized to analyze the principles in treating cancer cells with local high heat. In addition, it is expected that it can be applied to next-generation semiconductor technologies, such as wearable devices, transparent display devices, and analysis of local deterioration of power semiconductors, based on the principle of powerless operation.
DGIST Department of Electrical Engineering and Computer Science Professor Kang Hong-gi said, “It is significant in that we proposed a technology that directly and precisely measures the photothermal effect, the biggest advantage of which is rapid generation of local heat,” and added, “We look forward to the possibility of in-depth bioengineering analysis and biomedical application by combining it with various bio-electronic chips through micro-semiconductor processes in the future.”
The study was published online in Materials Horizons,
More information: Junhee Lee et al, High temporal resolution transparent thermoelectric temperature sensors for photothermal effect sensing, Materials Horizons (2022). DOI: 10.1039/D2MH00813K
Chemists are often the unsung heroes of scientific breakthroughs that change our lives. Credit: Matej Kastelic/Shutterstock
Real-world technology is often foretold by science fiction. In 1927, characters in the film Metropolis made video calls to each other. Star Trek creator Gene Roddenberry hung flat-screen color monitors on the walls of the Enterprise decades before we did the same in our living rooms.
The most obvious examples of technology in science fiction tend to focus on artificial intelligence, communication and transport. But futuristic chemistry is embraced by sci-fi writers too. For example, a central feature of Aldous Huxley’s 1932 novel Brave New World is a chemical antidepressant.
In recent years we’ve seen incredible leaps in chemical technologies—to the point where, as a chemist, I’m frequently reminded of some of my favorite fiction while reading about the latest big developments.
A plastic world
While environmental issues are a common thread in science fiction, not many deal with the blight of plastics. An exception is the 1972 novel Mutant 59: The Plastic Eaters. This story, featuring a bacteria that digests plastic, would have seemed far fetched a few years ago. After all, plastics have only been around for 80 years or so, which hardly seems long enough for nature to evolve a mechanism to eat them.
Yet plastics are carbon-based compounds, in many ways similar to natural polymers such as collagen (in animals), cellulose (in plants) and bee waxes. Over eons, bacteria and fungi have evolved many biochemical tools to scavenge the carbon from every dead organism.
So maybe it shouldn’t have been a surprise when, in 2016, scientists sifting through a recycling plant in Kyoto, Japan discovered a bacteria literally feeding on plastic bottles. Since then, several other research groups have isolated the digestive enzymes involved and engineered them to be more efficient. The hope is we can use these modified natural systems to clean up our plastic mess.
The most recent attempts to do so have a distinctly futuristic feel. A group in Austin, Texas fed the digestive enzymes’ structure into a neural network. This artificial intelligence predicted the best parts of the enzyme to tweak to increase its efficiency. With the AI’s advice, the group produced an enzyme that completely degraded a plastic punnet in just a couple of days.
Chemical engineers are already developing large-scale recycling plants using bacteria. The bacteria in Mutant 59 was also engineered in a lab—but let’s hope the parallel stops there. In the novel, the bacteria escapes and causes devastation as it rips through our world, rotting the plastic infrastructure that holds society together.
Many fake meats already line our supermarket shelves, but most are formed from plant-based ingredients blended to mimic the taste and texture of flesh. As a vegetarian, I actually quite enjoy them. But they are easily distinguishable from the real meat of my memories.
Growing meat in a vat is a different affair. It is more like brewing, but using animal cells instead of yeast. The process needs people with a good understanding of cell biology, nutritional chemistry and chemical engineering to work.
The process begins by growing a dense broth of cells. The mix of nutrients within the vat is changed, triggering the cells to differentiate into tissue types—muscle, connective tissue, fat cells. Finally, the cells coalesce into something resembling a pulp of meat, which is harvested and processed into your nuggets, burgers and such like. The advantage, of course, is that you get something with the texture, taste and nutritional content of meat, but without the slaughter.
Back in 2013, the first edible burger made this way cost $300,000. Nine years later, costs have plummeted and investors have in poured billions of dollars. The industry is poised to start selling its products, and is just waiting for the regulatory frameworks to be put in place. Singapore led the way in approving cultured meat in 2021, the US Food and Drug Administration recently gave its seal of approval, and UK and EU regulators are not far behind.
A word of caution
However, sometimes aspirations of real-world science struggle to progress from their fictional inspiration. In 2003 Elizabeth Holmes, aged only 19, founded Theranos. Ten years later, the company was worth $10 billion.
Holmes raised the funds with her promise to deliver a revolutionary technology that could deliver cheap, rapid diagnostics from just a drop of blood. The idea seemed closer to the medical scanners in Star Trek sickbays than anything in reality. And it turned out the promises made by Holmes were criminally over-inflated, earning her an 11-year prison sentence for fraud.
The Theranos story may have set back investors’ confidence in plausible applications for the lab-on-a-chip technologies that Holmes championed. But we are actually quite familiar with them already, in the form of COVID lateral flow tests. An even more extraordinary, real example reminded me of the almost-instant DNA sequencing depicted in the 1997 film Gattaca.
Early in 2022 at Stanford University, a small group of researchers sequenced an entire human genome in just over five minutes. Contrast that to the 13 years it took to sequence the first human genome, published in 2003. This could help speed up rare disease diagnosis from years to hours.
These astounding leaps forward in diagnostics, recycling and food are just a few areas of chemistry that were once considered science fiction. Many others—such as high-density batteries allowing quicker and fewer charges, atmospheric cleaning technology to remove C0₂ from the air, and 3D “printed” personalized medication—are also under development. Let’s just hope the dystopias so often depicted in science fiction don’t emerge alongside the technologies they describe.
31P NMR results for phosphate-containing species. (A) 1D NMR spectra from 10 mM sample in (D) taken at every 10 K showing line broadening in orthophosphate. (B) Linewidths for orthophosphate, pyrophosphate, ADP, and ATP as a function of temperature showing monotonic increase with temperature. Solid lines are quadratic fits to data to guide the eye. (C) R1 and R2 curves as a function of molecular tumbling rate from Bloembergen–Purcell–Pound theory. Cartoons illustrate the approximate locations of ionic phosphate, ADP, and a standard protein based on tumbling rates. (D) R2 as extracted from a CPMG pulse sequence and from FWHM for 10 mM and 100 mM monobasic sodium orthophosphate pH 4.5 as a function of temperature, showing monotonic increase in R2 in each case. Solid lines are quadratic fits to data to guide the eye. R1 for 10 mM, 100 mM, monobasic sodium orthophosphate pH 4.5 as a function of temperature showing different curve shapes as a function of concentration. Solid lines are cubic fits to data to guide the eye. Credit: Proceedings of the National Academy of Sciences (2022). DOI: 10.1073/pnas.2206765120
While conducting an otherwise straightforward investigation into the assembly mechanism of calcium-phosphate clusters, researchers at UC Santa Barbara and New York University (NYU) made a surprising discovery: Phosphate ions in water have a curious habit of spontaneously alternating between their commonly encountered hydrated state and a mysterious, previously unreported ‘dark’ state.
This recently uncovered behavior, they say, has implications for understanding the role of phosphate species in biocatalysis, cellular energy balance and the formation of biomaterials. Their findings are published in the Proceedings of the National Academy of Sciences
“Phosphate is everywhere,” said UCSB chemistry professor Songi Han, one of the authors of a paper in the Proceedings of the National Academy of Sciences. The ion consists of one phosphorus atom surrounded by four oxygen atoms. “It’s in our blood and in our serum,” Han continued. “It’s in every biologist’s buffer, it’s on our DNA and RNA.” It’s also a structural component of our bones and cell membranes, she added.
When bound with calcium, phosphates form small, molecular clusters on their way toward forming mineral deposits in cells and bone. That’s what Han and collaborators Matthew Helgeson at UCSB and Alexej Jerschow at NYU were preparing to study and characterize, in hopes of uncovering quantum behaviors in symmetric phosphate clusters proposed by UCSB physics professor Matthew Fisher. But first, the researchers had to set up control experiments, which involved scans of phosphate ions in the absence of calcium via nuclear magnetic resonance (NMR) spectroscopy and cryogenic transmission electron microscopy (cryo-TEM).
But as the UCSB and NYU students on the project were collecting reference data, which involved the naturally occurring isotope phosphorus 31 in aqueous solutions at varying concentrations and temperatures, their results didn’t match up with expectations. For instance, Han said, the line that represents the spectrum for 31P during NMR scans is supposed to narrow with increasing temperatures.
“The reason is, as you go to higher temperatures, the molecules tumble faster,” she explained. Typically, this rapid molecular motion would average out the anisotropic interactions, or interactions that are dependent on the relative orientations of these small molecules. The result would be a narrowing of resonances measured by the NMR instrument.
“We were expecting a phosphorus NMR signal, which is a simple one, with a peak that narrows with higher temperatures,” she said. “Surprisingly, though, we measured spectra that were broadening, doing the complete opposite of what we expected.”
This counterintuitive result set the team on a new path, following experiment after experiment to determine its molecular-level cause. The conclusion, after a year of eliminating one hypothesis after another? Phosphate ions were forming clusters under a wide range of biological conditions—clusters that were evading direct spectroscopic detection, which is likely why they had not been observed before. Furthermore, the measurements suggested these ions were alternating between a visible “free” state and a dark “assembled” state, hence the broadening of the signal instead of a sharp peak.
Evidence of phosphate assemblies from TEM and MD simulations. (A and B) TEM images of phosphate assemblies (yellow arrows) after heating phosphate solutions show droplet-like features forming at 25 to 50 nm in size. Samples were from different sources and prepared on different days. (A) 100 mM potassium ADP heated to 343 K before vitrification. (B) 100 mM sodium ADP heated to 343 K before vitrification. (C) Cluster size distributions from MD simulations at 343 K show the fraction, P(N), of phosphate ions in a cluster of size Nclust. The insets show snapshots of phosphate assemblies (red and white) and sodium ions (blue) from the simulations. The cluster size distribution and snapshots show that HPO42− strongly assembles in contrast to H2PO4−. When H2PO4− is mixed with HPO42−, the latter induces clustering of H2PO4−. In this mixed system, the HPO42− ions are grayed out to highlight the clustering of H2PO4−. Simulation snapshots are visualized using Visual Molecular Dynamics. Credit: Proceedings of the National Academy of Sciences (2022). DOI: 10.1073/pnas.2206765120
Additionally, as the temperature increased, the number of these assembled states was also increasing, another temperature-dependent behavior, according to co-lead author Mesopotamia Nowotarski.
“The conclusion from those experiments was that the phosphates are dehydrating and that allows them to come closer together,” she said. At lower temperatures, the vast majority of these phosphates in solution cling to water molecules that form a protective water coat around them. This hydrated state is typically assumed when considering how phosphate behaves in biological systems.
But at higher temperatures, Nowotarski explained, they shed their water shields, allowing them to stick to each other. This concept was confirmed by NMR experiments that probed the phosphate water shell, and further validated by analysis of cryo-TEM images to identify the existence of clusters, as well as modeling the energetics of phosphate assembly by co-lead author Joshua Straub.
These dynamic phosphate assemblies and hydration shells have important implications for biology and biochemistry, according to the researchers. Phosphate, said chemical engineer Matthew Helgeson, is a commonly understood “currency” used in biological systems to store and consume energy through conversion into adenosine triphosphate (ATP) and adenosine diphosphate (ADP).
“If hydrated phosphate, ADP and ATP represent small ‘bills’ of currency, this new discovery suggests that these smaller currencies can exchange with much larger denominations—say $100—which may have very different interactions with biochemical processes than currently known mechanisms,” he said.
Also, many biomolecular components include phosphate groups that may, similarly, form clusters. Hence, the finding that these phosphates can spontaneously assemble might shed some light on other fundamental biological processes such as biomineralization—how shells and skeletons form, as well as protein interactions.
“We also tested a range of phosphates, including those incorporated into the ATP molecule, and they all appear to show the same phenomenon, and we achieved quantitative analysis for these assemblies,” said co-lead author Jiaqi Lu.
This once overlooked process could also be significant in the realms of cell signaling, metabolism and disease processes such as Alzheimer’s disease, where the attachment of a phosphate group, or phosphorylation, to the protein tau in our brain is commonly found in neurofibrillary tangles—a hallmark of neurodegeneration. Having seen and studied this assembly behavior, the team is now digging deeper, with studies on the effect of pH on phosphate assembly, genetic translation and modified protein assembly, as well as their original work on calcium phosphate assembly.
“It really changes the way we think about the role of phosphate groups that we typically don’t consider a driver of molecular assembly,” Han said.
More information: Joshua S. Straub et al, Phosphates form spectroscopically dark state assemblies in common aqueous solutions, Proceedings of the National Academy of Sciences (2022). DOI: 10.1073/pnas.2206765120
The significant increase in intensity of reflected light for a particular angle of incidence is represented as a peak, and its pattern can be used as a “fingerprint/feature of a compound.” One peak pattern consisting of many peaks corresponds to one compound. The test data diffraction pattern (red curve) is overlaid with the auto-encoder output (blue curve) representing the relevance of peaks in characterization. The peak labeled (a) has significant intensity but low relevancy, a Deep Learning result. Credit: Ryo Maezono from JAIST
X-ray diffraction (XRD) is an experimental technique to discern the atomic structure of a material by irradiating it with X-rays at different angles. Essentially, the intensity of the reflected X-rays becomes high at specific irradiation angles, producing a pattern of diffraction peaks. An XRD serves as a fingerprint for a material since each substance produces a unique pattern.
In research and development, changes in XRDs are used to identify the positions and amounts of additional elements that need to be added to fine-tune a material to help enhance a desired functional property, say, energy storage efficiency in batteries.
However, the peak changes in XRDs are barely discernible to humans. This makes ascertaining the features and relevance of different peaks for material characterization difficult. To this end, a group of Japanese researchers, led by Professor Ryo Maezono from the Japan Advanced Institute of Science and Technology (JAIST), applied a Deep Learning technique called “auto-encoder” to the problem to find hidden regularities in XRDs that could help accelerate the development of new functional materials.
The research team also included Associate Professor Kenta Hongo and Assistant Professor Kousuke Nakano from JAIST. Their work has been published in Advanced Theory and Simulations.
Explaining the fundamentals of the auto-encoder technique, Prof. Maezono says, “The auto-encoder technique captures data features by expressing them as points on a two-dimensional plane (feature space). Based on their scatter, the points get grouped to coarse-grain information. The auto-encoder compresses the data dimension and can efficiently capture the multifaceted XRD pattern analysis in a two-dimensional plane.”
Using a neural network, the researchers applied the auto-encoder to 150 XRD patterns of magnetic alloys with different concentrations. In the feature space, each XRD is projected to a single point. These points form clusters, in which similar materials with similar constituent concentrations are placed closer together. Thus, the distance between the points in the feature space allows the estimation of the concentration of any given sample alloy. This also permits the fine-tuning of alloys by indirectly identifying the XRD peaks that change when new elements are added to an alloy or its constituent element ratios are altered.
The researchers further proposed a novel application of the feature space. When a peak of interest is masked on the original XRD pattern, the point on the feature space shifts. The extent of the shift helps distinguish how relevant a peak is to capturing the properties of a material. Using this technique, the researchers were able to identify which peak is actually relevant to be watched out for estimating the amount of doping etc.—something that could not have been predicted by a human but was revealed using Deep Learning.
The researchers also proposed the application of the auto-encoder for the generation of artificial XRD patterns by interpolating existing ones to handle tiny changes in alloy compositions. The approach would generate plausible datasets, avoiding computationally expensive ab initio simulations.
“The results of this research are not limited to XRD peak patterns. Rather, they provide a general Deep Learning technique that can be used to extract features from material science data. Its framework can find hidden regularity in nature that is not identifiable by humans and is expected to serve as a powerful tool for theorem discovery through data science,” says Prof. Maezono.
The application of the described auto-encoder could accelerate the development of high efficiency, low cost, and low environmental impact materials, ushering in a new era of Deep Learning-based materials science research.
More information: Keishu Utimula et al, Feature Space of XRD Patterns Constructed by an Autoencoder, Advanced Theory and Simulations (2022). DOI: 10.1002/adts.202200613
Provided by Japan Advanced Institute of Science and Technology
Proposed chemical recycling of waste polyolefins and this work on transformation of post-consumer waste polyethylene into chemically recyclable materials. Credit: Journal of the American Chemical Society (2022). DOI: 10.1021/jacs.2c11949
Researchers at the U.S. Department of Energy’s (DOE) Institute for Cooperative Upcycling of Plastics (iCOUP) have developed a new method for recycling high-density polyethylene (HDPE).
Using a novel catalytic approach, scientists at DOE’s Argonne National Laboratory and Cornell University converted post-consumer HDPE plastic into a fully recyclable and potentially biodegradable material with the same mechanical and thermal properties of the starting single-use plastic. Their paper describing the results was published December 16 in the Journal of the American Chemical Society.
HDPE is ubiquitous in single-use applications because it is strong, flexible, long-lasting and inexpensive. But the ways we produce and dispose of HDPE pose serious threats to our own health and that of our planet.
Many HDPE products are produced from fossil fuels, and most post-consumer HDPE is either incinerated, dumped in landfills or lost in the environment. When it is recycled with current methods, the quality of the material degrades.
This new approach could reduce carbon emission and pollution associated with HDPE by using waste plastic as untapped feedstock and transforming it into a new material that can be recycled repeatedly without loss of quality.
Current HDPE recycling approaches yield materials with inferior properties. The team’s alternative approach uses a series of catalysts to cleave the polymer chains into shorter pieces that contain reactive groups at the ends. The smaller pieces can then be put back together to form new products of equal value. The end groups have the added benefit of making the new plastic easier to decompose, both in the lab and in nature.
More information: Alejandra Arroyave et al, Catalytic Chemical Recycling of Post-Consumer Polyethylene, Journal of the American Chemical Society (2022). DOI: 10.1021/jacs.2c11949
A new tool from the Imperiali Lab uses directed evolution to generate glycan-binding proteins (GBPs) from small, hyper-thermostable DNA-binding protein. Credit: Massachusetts Institute of Technology
One of the major obstacles that those conducting research on carbohydrates are constantly working to overcome is the limited array of tools available to decipher the role of sugars. As a workaround, most researchers utilize lectins (sugar-binding proteins) isolated from plants or fungi, but they are large, with weak binding, and they are limited in their specificity and in the scope of sugars that they detect.
In a new study published in ACS Chemical Biology, researchers in Professor Barbara Imperiali’s group have developed a platform to address this shortcoming.
“The challenge with polymers of carbohydrates is that their biosynthesis is not template-driven,” says Imperiali, the senior author of the study and a professor in the departments of Chemistry and Biology. “Biology, medicine, and biotechnology have been fueled by technological advancements for proteins and nucleic acids. The carbohydrate field lags terribly behind and is desperately seeking tools.”
Identifying carbohydrate-binding proteins
Biosynthesizing carbohydrates requires every link between individual sugar molecules to be made by a particular enzyme, and there’s no ready way to decipher the structures and sequences of complex carbohydrates. Antibodies to carbohydrates can be generated, but doing so is challenging, expensive, and results in a molecule that is far larger than what is really needed for the research.
An ideal resource for this field plagued with limited mechanisms would be discovery of binding proteins, of limited size, that recognize small chunks of carbohydrates to piece together a structure by using those binders, or methods to detect and identify particular carbohydrates within complicated structures.
The authors of this study used directed evolution and clever screen design to identify carbohydrate-binding proteins from proteins that have absolutely no ability to bind carbohydrates at all. Their findings lay the groundwork for identifying carbohydrate-binding proteins with diverse and programmable specificity.
Streamlining for collaboration
This advance will allow researchers to go after a user-defined sugar target without being limited by what a lectin does, or challenged by the abilities of generating antibodies. These results could serve to inspire future collaborations with engineering communities to maximize the efficiency of glycobiology’s yeast surface display pipeline. As it is, this pipeline works well for proteins, but sugars are far more difficult targets and require the pipeline to be modified.
In terms of future applications, the potential for this innovation ranges from diagnostic to, in the longer term, therapeutic, and paves the way for collaborations with researchers at MIT and beyond. For example, chemistry Professor Laura Kiessling’s research group works with Mycobacterium tuberculosis (Mtb), which has an unusual cell wall composition with unique, distinct, and exclusive sugars. Using this method, a binder could potentially be evolved to that particular feature on Mtb.
Chemical engineering Professor Hadley Sikes develops paper-based diagnostic tools where the binding partner for a particular epitope or marker is laid down, and with the use of this discovery, in the longer term, a lateral flow assay device could be developed.
Laying the groundwork for future solutions
In cancer, certain sugars are overrepresented on cell surfaces, so theoretically, researchers can utilize this finding, which is also amenable to labeling, to develop a tool out of the evolved glycan binder for detection.
This discovery also stands to contribute significantly to improving cell imaging. Researchers can modify binders with a fluorophore using a simple ligation strategy, and can then choose the best fluorophore for tissue or cell imaging. The Kiessling group, for example, could apply small protein binders labeled with fluorophore to detect bacterial sugars to initiate fluorescence-activated cell sorting to probe a complex mixture of microbes.
This could in turn be used to determine how a patient’s microbiome has been disturbed. It also has the potential to screen the microbiome of a patient’s mouth or their upper or lower gastrointestinal tract to read out the imbalance within the community using these types of reagents. In the more distant future, the binders could potentially have therapeutic purposes like clearing the gastrointestinal tract or mouth of a particular bacterium based on the sugars that the bacterium displays.
More information: Elizabeth M. Ward et al, Engineered Glycan-Binding Proteins for Recognition of the Thomsen–Friedenreich Antigen and Structurally Related Disaccharides, ACS Chemical Biology (2022). DOI: 10.1021/acschembio.2c00683
The separation of carbazole/anthracene with N, N-dimethylformamide: NMR study substantiated carbazole separation. Credit: Yan Qiao, Institute of Coal Chemistry, Chinese Academy of Sciences
Coal tar, once considered waste, has become a huge treasure trove because hundreds of compounds can be isolated from it. Most of these compounds tend to be aromatic hydrocarbons, polycyclic aromatic hydrocarbons, and heterocyclic compounds.
Carbazole and anthracene, two aromatic hydrocarbon components contained in coal tar, are used as essential organic intermediates to synthesize various carbazole derivatives and anthraquinones. The effective separation of carbazole and anthracene takes advantage of their different solubility in solvents. In this process, solvent screening and performance optimization are essential, and their optimization mainly follows the principle of trial and error. Thus, it is necessary to use a versatile detection technique for understanding this separation process on the molecular level.
N,N-Dimethylformamide (DMF) has been developed as an efficient solvent for carbazole and anthracene separation due to the high solubility of carbazole in DMF; moreover, researchers have found that the separation of carbazole and anthracene may benefit from an intermolecular hydrogen bond between carbazole and DMF. However, there was no detailed study concerning the interaction mechanism between carbazole/anthracene and a solvent capable of hydrogen bonding. It is important to use a versatile detection technique for analyzing hydrogen bond interaction, and hence to explain the interaction mechanism between carbazole/anthracene and DMF via hydrogen bonding on the molecular level.
Recently, the group of Yan Qiao, a professor of the State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, CAS, studied the intermolecular interaction mechanism between DMF and carbazole/anthracene by various advanced liquid state NMR techniques. They observed that the N-H chemical shift of carbazole changed significantly in 1H NMR titration and VT-NMR experiments, indicating strong intermolecular hydrogen bonds between carbazole and DMF, which was further supported by the decrease in molecular self-diffusion coefficients (D) of both carbazole and DMF according to diffusion-ordered spectroscopy (DOSY) measurements.
Moreover, the Nuclear Overhauser Effect Spectroscopy (NOESY) experiment revealed that the distance between the aldehydic hydrogen of DMF and the N-H of carbazole was smaller than 5 Å. Accordingly, an intermolecular hydrogen bond between carbazole and DMF in the form of C=O···H-N was proposed.
“Solvent screening is still lacking theoretical guidance, with most work on the basis of ‘like dissolves like’ and lacks direct spectroscopic evidence,” Qiao said, “Our research helps researchers to understand the interaction mechanism between carbazole/anthracene and DMF in this process from the molecular and even atomic levels. It will also guide the further expansion of alternative solvent media and optimization of separation processes, and play an important role in promoting the development of coal tar separation industry.”
This research is published in the journal Industrial Chemistry & Materials.
More information: Hui Cao et al, Understanding the interaction mechanism of carbazole/anthracene with N,N-dimethylformamide: NMR study substantiated carbazole separation, Industrial Chemistry & Materials (2022). DOI: 10.1039/D2IM00020B
Provided by Institute of Process Engineering, Chinese Academy of Sciences
UMAP plot visualizing the distribution of the polymer backbones. The UMAP plots show the distribution of (a) 15,335 homopolymers in PoLyInfo and (b) 1070 homopolymers calculated in this study. The 21 classes of the polymer backbones are color-coded according to the definition of PoLyInfo. Credit: npj Computational Materials (2022). DOI: 10.1038/s41524-022-00906-4
A research team has published their method to create a comprehensive database of polymer properties, as well as experimental validation, in npj Computational Materials.
“Materials informatics (MI), a new branch of materials research that combines materials data with data science, is gaining traction,” said co-corresponding author Yoshihiro Hayashi, assistant professor, Institute of Statistical Mathematics in the Research Organization of Information and Science (ROIS). Hayashi is also affiliated with the University of Tokyo’s Department of Mechanical Engineering. “MI applies machine learning to predict new materials with innovative properties and their fabrication methods from a vast design space. As such, data is the most important resource in MI.”
Despite the need, Hayashi said, efforts to create a comprehensive database of polymer properties to enable data-driven research have fallen short.
“To construct a database of polymer properties by molecular simulations, we developed RadonPy,” Hayashi said. “It’s the first open-source software that successfully automates polymer physical property calculations using simulations of classical molecular dynamics based on atomistic models—which account for the behaviors and characteristics of individual constituents.”
The program takes an assigned polymer and runs calculations to equilibrate it in prescribed system parameters. Once it does, it can then calculate the polymer’s density, radius of gyration, refractive index, thermal conductivity, specific heat capacities at constant pressure and at constant volume, among other information. RadonPy produces and stores the data, which can then be accessed later. The researchers also implemented a machine learning technique called transfer learning to correct biases and variations between the simulated property values and experimental data.
“In this study, more than 1,000 unique amorphous polymers were computed in about two months, mainly using the supercomputer Fugaku,” said co-corresponding author Ryo Yoshida, professor, Institute of Statistical Mathematics in ROIS, the National Institute for Materials Science’s Research and Services Division of Materials Data and Integrated System and The Graduate University of Advance Studies’ Department of Statistical Science.
“The program implements a set of automatic computation functions for 15 different properties, which were systematically compared with experimental data to validate the calculation conditions. We also comprehensively verified the agreement between six properties obtained from high-throughput molecular dynamics calculations and experimental values.”
The research team also identified eight amorphous polymers with high conductivity, according to Yoshida. Now, the group is using RadonPy to create the world’s largest open database of polymer physics, with more than 100,000 different polymer species. In addition to ROIS, three universities and 19 companies are partnering to jointly develop other databases with RadonPy for a variety of applications in academia and industry.
“This project will create a world map of polymer material properties,” Hayashi said. “Such exhaustive observations cannot be achieved solely via experimental approaches requiring considerable costs, such as in material synthesis. This research is the first step toward a new horizon of polymer science.”
More information: Yoshihiro Hayashi et al, RadonPy: automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics, npj Computational Materials (2022). DOI: 10.1038/s41524-022-00906-4
Provided by Research Organization of Information and Systems
A team from the Universitat Politècnica de València (UPV), the Universitat de València (UV), and the Centro de Investigación Biomédica en Red sobre Enfermedades Raras (CIBERER) has developed a lateral flow test that identifies and quantifies the level of allergens reliably in food with the help of a smartphone. The work has been published in the journal Biosensors.
“Food allergy or hypersensitivity is estimated to affect about 520 million people worldwide. These reactions occur mainly through the consumption of foods containing trace allergens. Therefore, identifying and quantifying them before the food is consumed is essential, and this is what the test we have developed allows,” says Sergi Morais, professor in the Department of Chemistry at the Universitat Politècnica de València and researcher at the Inter-University Institute of Molecular Recognition and Technological Development (IDM).
The prototype has been developed as a proof of concept for simultaneously detecting almond and peanut allergens and has been validated with everyday commercial foods such as biscuits and energy bars.
Among its advantages, the researchers highlight the reliability of the test, which contains multiple internal controls and calibrators integrated into a miniaturized 36-point array.
“With microarray technology, we perform 36 assays in a single step. The derived information allows us to identify whether the result is a true positive or negative. In addition, with the internal calibrators and the smartphone, we can quantify with high precision traces of allergen in the food,” says Ángel Maquieira, full professor in the Department of Chemistry at the Universitat Politècnica de València.
Regarding the extraction method, the UPV, UV, and CIBERER team stresses its simplicity, which means anyone can carry it out at any time.
“Current extraction methods consist of multiple steps and require sophisticated equipment for grinding, degreasing, extraction, and purification of allergens. Therefore, the analysis is carried out in qualified laboratories. The aim is to decentralize the analysis, as has been done with the COVID-19 test. We want anyone to be able to analyze a food just before consuming it,” adds Sergi Morais.
The extraction method developed is based on the use of a portable grinder, which is used to grind and filter the sample in a single step; 5 mL of a solution is then added to extract the allergen, and, once the sample is prepared, the test strip is immersed in the solution. And in just 5 minutes, the result is obtained, which can be read with a mobile phone.
“At an estimated cost of €1 per strip, the developed test has great commercial potential, for example, in the food sector for rapid identification of allergens in situ and in the pharmaceutical sector to quantify the potency of allergenic extracts used in allergy testing,” says Amadeo Sena, a postdoctoral researcher at the Inter-University Institute for Molecular Recognition and Technological Development (IDM).
Future development
Looking to the future, the UPV, UV, and CIBERER team points out that, given the characteristics of the test strip, it could easily be adapted for other allergens, as the group has specific antibodies for a wide range of allergens and biomarkers.
“Our challenge is to develop a test for the simultaneous quantification of the 14 allergens that must be declared according to Royal Decree 126/2015,” concludes Patricia Casino, a researcher at Instituto Universitario de Biotecnología i Biomedicina (BIOTECMED)—Universitat de València and the CIBERER.
More information: Amadeo Sena-Torralba et al, Lateral Flow Microimmunoassay (LFµIA) for the Reliable Quantification of Allergen Traces in Food Consumables, Biosensors (2022). DOI: 10.3390/bios12110980
The synthesis route for the controlled synthesis of Co-NiOx@GDY through a three-step strategy including the first growth of a film of cobalt-nickel bimetal mixed nanosheets on the surface of nickel foam (Co-NiOxHy), followed by a calcination treatment to obtain Co-NiOx, and finally the in-situ growth of ultrathin GDY films on the surface of Co-NiOx through a cross-coupling reaction with hexaethynylbenzene (HEB) as the precursor. Credit: Science China Press
A major impediment to industrial urea synthesis is the lack of catalysts with high selectivity and activity. Prof. Yuliang Li (Institute of Chemistry, Chinese Academy of Sciences) and coworkers reported a new catalyst system suitable for the highly selective synthesis of industrial urea by in-situ growth of graphdiyne on the surface of cobalt-nickel mixed oxides.
The researchers found that such a catalyst is a multi-heterojunction interfacial structure resulting in the obvious incomplete charge transfer phenomenon between graphdiyne and metal oxide interface and multiple intermolecular interactions. These intrinsic characteristics are the origin of the high performance of the catalyst.
The team also demonstrated that the catalyst could effectively optimize the adsorption/desorption capacities of the intermediate and promote the direct C-N coupling by significantly suppressing by-product reactions toward the formation of H2, CO, N2, NH3.
The catalyst can selectively synthesize urea directly from nitrite and carbon dioxide in water at room temperature and pressure and exhibits record-high Faradaic Efficiency (FE) of 64.3%, nitrogen selectivity (Nurea-selectivity) of 86.0%, carbon selectivity (Curea-selectivity) of ~100%, as well as the urea yield rates of 913.2 μg h–1 mgcat–1 and remarkable long-term stability.
The work is published in the journal National Science Review.
More information: Danyan Zhang et al, Multi-heterointerfaces for selective and efficient urea production, National Science Review (2022). DOI: 10.1093/nsr/nwac209
