The LipidOz software helps researchers process the complicated data obtained from their instruments and assigns the locations of double bonds in lipids. Repetitive and difficult analyses can be automated with LipidOz. Credit: Stephanie A. King and Nathan Johnson | Pacific Northwest National Laboratory
Lipids are a class of biomolecules that play an important role in many cellular processes. Analyses that seek to characterize all lipids in a sample—called lipidomics—are crucial to studying complex biological systems.
An important challenge in lipidomics is connecting the variety of structures of lipids with their biological functions. The positions of the double bonds within fatty acid chains is particularly important. This is because they can affect the physical properties of cellular membranes and modulate cell signaling pathways.
This information is not routinely measured in lipidomics studies because it requires a complicated experimental setup that produces complex data. Thus, scientists at Pacific Northwest National Laboratory (PNNL) developed a streamlined workflow to determine the positions of double bonds. This workflow uses both automation and machine learning approaches.
Their new method, LipidOz, streamlines the data analysis to determine the positions of double bonds. By addressing this key part of the analysis of lipids, LipidOz offers researchers a more efficient and accurate method for lipid characterization. The study is published in the journal Communications Chemistry.
The unambiguous identification of lipids is complicated by the presence of molecular parts that have the same chemical formula but different physical configurations. Specifically, the differences in these molecular parts include the fatty acyl chain length, stereospecifically numbered (sn) position, and position/stereochemistry of double bonds.
Conventional analyses can determine the fatty acyl chain lengths, the number of double bonds, and—in some cases—the sn position but not the positions of carbon–carbon double bonds. The positions of these double bonds can be determined with greater confidence using a gas-phase oxidation reaction called ozone-induced dissociation (OzID), which produces characteristic fragments.
However, the analysis of the data obtained from this reaction is complex and repetitive, and there is lack of software tool support. The open-source Python tool, LipidOz, automatically determines and assigns the double bond positions of lipids using a combination of traditional automation and deep learning approaches. New research demonstrates this ability for standard lipid mixtures and complex lipid extracts, enabling practical application of OzID for future lipidomics studies.
The “ten electron rule” provides guidance for the design of single-atom alloy catalysts for targeted chemical reactions.
A collaborative team across four universities has discovered a very simple rule to design single-atom alloy catalysts for chemical reactions. The “ten electron rule” helps scientists identify promising catalysts for their experiments very rapidly. Instead of extensive trial and error experiments of computationally demanding computer simulations, catalysts’ composition can be proposed simply by looking at the periodic table.
Single-atom alloys are a class of catalysts made of two metals: a few atoms of reactive metal, called the dopant, are diluted in an inert metal (copper, silver or gold). This recent technology is extremely efficient at speeding up chemical reactions but traditional models don’t explain how they work.
The team, which worked across the University of Cambridge, University College London, the University of Oxford and the Humboldt-University of Berlin, has published their research in Nature Chemistry. The scientists made computer simulations to unravel the underlying laws that control how single-atom alloy catalysts work.
The rule showed a simple connection: chemicals bind the most strongly to single-atom alloy catalysts when the dopant is surrounded by ten electrons. This means that scientists designing experiments can now simply use the columns on the periodic table to find which catalysts will have the desired properties for their reactions.
Dr. Romain Réocreux, a postdoctoral researcher in the group of Prof. Angelos Michaelides, who led this research, says, “When you have a difficult chemical reaction, you need a catalyst with optimal properties. On the one hand, a strong-binding catalyst may poison and stop accelerating your reaction; on the other hand, a weakly-binding catalyst may just do nothing.”
“Now we can identify the optimal catalyst just by looking at a column on the periodic table. This is very powerful since the rule is simple and can speed up the discovery of new catalysts for particularly difficult chemical reactions.”
Prof. Stamatakis, Professor of Computational Inorganic Chemistry at the University of Oxford, who contributed to the research, says, “After a decade of intense research on single-atom alloys, we now have an elegant, simple but powerful theoretical framework that explains binding energy trends and enables us to make predictions about catalytic activity.”
Using this rule, the team proposed a promising catalyst for an electrochemical version of the Haber-Bosch process, a key reaction for the synthesis of fertilizers that has been using the same catalyst since it was first discovered in 1909.
Dr. Julia Schumann, who started the project at the University of Cambridge and is now at Humboldt-Universität of Berlin, explains, “Many catalysts used in the chemical industry today were discovered in the laboratory using trial and error methods. With a better understanding of the materials’ properties, we can propose new catalysts with improved energy efficiency and reduced CO2 emissions for industrial processes.”
The migration of hydrogen in a pure magnesium layer was studied with electron spectroscopy in the ultra-high vacuum chamber in Dübendorf. Credit: Empa / AB / IFJ PAN
It is easy to be optimistic about hydrogen as an ideal fuel. It is much more difficult to come up with a solution to an absolutely fundamental problem: How to store this fuel efficiently? A Swiss-Polish team of experimental and theoretical physicists has found the answer to the question of why previous attempts to use the promising magnesium hydride for this purpose have proved unsatisfactory, and why they may succeed in the future.
Hydrogen has long been seen as the energy carrier of the future. However, before it becomes a reality in the energy sector, efficient methods of storing it must be developed. Materials—selected in such a way that at low energy cost, hydrogen can first be injected into them and then recovered on demand, preferably under conditions similar to those typical of our everyday environment—appear to be the optimal solution.
A promising candidate for hydrogen storage appears to be magnesium. Converting it into magnesium hydride, however, requires a suitably efficient catalyst, which has not yet been found.
The work of a team of scientists from Empa—the Swiss Federal Laboratories for Materials Science and Technology in Dübendorf, and the Department of Chemistry at the University of Zurich as well as the Institute of Nuclear Physics of the Polish Academy of Sciences (IFJ PAN) in Cracow, has shown that the reason for the many years of failure up to this point lies in an incomplete understanding of the phenomena occurring in magnesium during hydrogen injection.
The main obstacle to the uptake of hydrogen as an energy source is the difficulty of storing it. In still-rare hydrogen-powered cars, it is stored compressed at a pressure of around 700 atmospheres. This is neither the cheapest nor the safest method, and it has little to do with efficiency: There is only 45 kg of hydrogen in one cubic meter. The same volume can hold 70 kg of hydrogen, if it is condensed beforehand.
Unfortunately, the liquefaction process requires large amounts of energy, and the extremely low temperature, at around 20 Kelvin, must then be maintained throughout storage. An alternative could be suitable materials; for example, magnesium hydride, which can hold up to 106 kg of hydrogen in a cubic meter.
Magnesium hydride is among the simplest of the materials tested for hydrogen storage capacity. Its content can reach 7.6% (by weight). Magnesium hydride devices are therefore quite heavy and so mainly suitable for stationary applications. However, it is important to note that magnesium hydride is a very safe substance and can be stored without risk; for example, in a basement, and magnesium itself is a readily available and cheap metal.
“Research on the incorporation of hydrogen into magnesium has been going on for decades, yet it has not resulted in solutions that can count on wider use,” says Prof. Zbigniew Lodziana (IFJ PAN), a theoretical physicist who has co-authored an article in Advanced Science, where the latest discovery is presented.
“One source of problems is hydrogen itself. This element can effectively penetrate the crystal structure of magnesium, but only when it is present in the form of single atoms. To obtain it from typical molecular hydrogen, a catalyst efficient enough to make the process of hydrogen migration in the material fast and energetically viable is required. So everyone has been looking for a catalyst that meets the above conditions, unfortunately without much success. Today, we finally know why these attempts were doomed to failure.”
Prof. Lodziana has developed a new model of the thermodynamic and electron processes occurring in magnesium in contact with hydrogen atoms. The model predicts that during the migration of hydrogen atoms, local, thermodynamically stable magnesium hydride clusters are formed in the material. At the boundaries between the metallic magnesium and its hydride, changes in the electronic structure of the material then occur, and it is these that have a significant role in reducing the mobility of hydrogen ions.
In other words, the kinetics of magnesium hydride formation is primarily determined by phenomena at its interface with magnesium. This effect had so far not been taken into account in the search for efficient catalysts.
Prof. Lodziana’s theoretical work complements experiments performed in the Swiss laboratory in Dübendorf. Here, the migration of atomic hydrogen in a layer of pure magnesium sputtered onto palladium was studied in an ultra-high vacuum chamber. The measuring apparatus was capable of recording changes in the state of several outer atomic layers of the sample under study, caused by the formation of a new chemical compound and the associated transformations of the material’s electronic structure. The model proposed by the researchers from the IFJ PAN allows us to fully understand the experimental results.
The achievements of the Swiss-Polish group of physicists not only pave the way for a new search for an optimal catalyst for magnesium hydride, but also explain why some of the previously found catalysts showed higher efficiency than expected.
“There is much to suggest that the lack of significant progress in hydrogen storage in magnesium and its compounds was simply due to our incomplete understanding of the processes involved in hydrogen transport in these materials. For decades, we have all been looking for better catalysts, only not where we should be looking. Now, new theoretical and experimental results make it possible to think again with optimism about further improvements in methods of introducing hydrogen into magnesium,” concludes Prof. Lodziana.
More information: Selim Kazaz et al, Why Hydrogen Dissociation Catalysts do not Work for Hydrogenation of Magnesium, Advanced Science (2023). DOI: 10.1002/advs.202304603
RoboChem is an autonomous benchtop platform for fast, accurate and around-the-clock chemical synthesis. Credit: University of Amsterdam
Chemists of the University of Amsterdam (UvA) have developed an autonomous chemical synthesis robot with an integrated AI-driven machine learning unit. Dubbed “RoboChem,” the benchtop device can outperform a human chemist in terms of speed and accuracy while also displaying a high level of ingenuity.
As the first of its kind, it could significantly accelerate chemical discovery of molecules for pharmaceutical and many other applications. RoboChem’s first results are published in the journal Science.
RoboChem was developed by the group of Prof. Timothy Noël at the UvA’s Van ‘t Hoff Institute for Molecular Sciences. Their paper shows that RoboChem is a precise and reliable chemist that can perform a variety of reactions while producing minimal amounts of waste.
Working autonomously around the clock, the system delivers results quickly and tirelessly. Noël said, “In a week, we can optimize the synthesis of about ten to twenty molecules. This would take a Ph.D. student several months.” The robot not only yields the best reaction conditions, but also provides the settings for scale-up.
“This means we can produce quantities that are directly relevant for suppliers to the pharmaceutical industry, for example.”
Time lapse of RoboChem. Credit: University of Amsterdam
RoboChem’s ‘brain’
The expertise of the Noël group is in “flow chemistry,” a novel way of performing chemistry where a system of small, flexible tubes replaces beakers, flasks and other traditional chemistry tools.
In RoboChem, a robotic needle carefully collects starting materials and mixes these together in small volumes of just over half a milliliter. These then flow through the tubing system towards the reactor. There, the light from powerful LEDs triggers the molecular conversion by activating a photocatalyst included in the reaction mixture.
The flow then continues towards an automated NMR spectrometer that identifies the transformed molecules. These data are fed back in real time to the computer that controls RoboChem.
“This is the brain behind RoboChem,” says Noël. “It processes the information using artificial intelligence. We use a machine learning algorithm that autonomously determines which reactions to perform. It always aims for the optimal outcome and constantly refines its understanding of the chemistry.”
Impressive ingenuity
The group put a lot of effort into substantiating RoboChem’s results. All of the molecules now included in the Science paper were isolated and checked manually. Noël says the system has impressed him with its ingenuity.
“I have been working on photocatalysis for more than a decade now. Still, RoboChem has shown results that I would not have been able to predict. For instance, it has identified reactions that require only very little light. At times I had to scratch my head to fathom what it had done. You then wonder: would we have done it the same way? In retrospect, you see RoboChem’s logic. But I doubt if we would have obtained the same results ourselves. Or not as quickly, at least.”
The researchers also used RoboChem to replicate previous research published in four randomly selected papers. They then determined whether Robochem produced the same—or better—results.
“In about 80% of the cases, the system produced better yields. For the other 20%, the results were similar,” Noël says. “This leaves me with no doubt that an AI-assisted approach will be beneficial to chemical discovery in the broadest possible sense.”
RoboChem is based on the principles of Flow Chemistry. Reactions are carried out in volumes of just 650 microliter, flowing through small tubes. Credit: University of Amsterdam
Breakthroughs in chemistry using AI
According to Noël, the relevance of RoboChem and other “computerized” chemistry also lies in the generation of high-quality data, which will benefit the future use of AI.
“In traditional chemical discovery only a few molecules are thoroughly researched. Results are then extrapolated to seemingly similar molecules. RoboChem produces a complete and comprehensive dataset where all relevant parameters are obtained for each individual molecule. That provides much more insight.”
Another feature is that the system also records “negative” data. In current scientific practice, most published data only reflects successful experiments. “A failed experiment also provides relevant data,” says Noël.
“But this can only be found in the researchers’ handwritten lab notes. These are not published and thus unavailable for AI-powered chemistry. RoboChem will change that, too. I have no doubt that if you want to make breakthroughs in chemistry with AI, you will need these kinds of robots.”
Credit: Journal of the American Chemical Society (2023). DOI: 10.1021/jacs.3c09195
A University of Massachusetts Amherst team has made a major advance toward modeling and understanding how intrinsically disordered proteins (IDPs) undergo spontaneous phase separation, an important mechanism of subcellular organization that underlies numerous biological functions and human diseases.
IDPs play crucial roles in cancer, neurodegenerative disorders and infectious diseases. They make up about one-third of proteins that human bodies produce, and two-thirds of cancer-associated proteins contain large, disordered segments or domains. Identifying the hidden features crucial to the functioning and self-assembly of IDPs will help researchers understand what goes awry with these features when diseases occur.
In a paper published in the Journal of the American Chemical Society, senior author Jianhan Chen, professor of chemistry, describes a novel way to simulate phase separations mediated by IDPs, an important process that has been difficult to study and describe.
“Phase separation is a really well-known phenomenon in polymer physics, but what people did not know until about 15 years ago was that this is also a really common phenomenon in biology,” Chen explains. “You can look at phase separation with a microscope, but to understand this phenomenon at the molecular level is very difficult.
“In the past five or 10 years, people have started to discover that many of these disordered proteins can drive phase separation, including numerous important ones involved in cancer and neurodegenerative disorders.”
The new paper, based on research in Chen’s computational biophysics and biomaterials lab, constitutes one chapter of lead author Yumeng Zhang’s Ph.D. dissertation. Zhang will start work as a postdoctoral researcher at Massachusetts Institute of Technology (MIT) in February. Another key contributor is Shanlong Li, a postdoctoral research associate in Chen’s lab.
Chen’s lab developed an accurate, GPU-accelerated hybrid resolution (HyRes) force field for simulating phase separations mediated by IDPs. This model is unique in its ability to accurately describe peptide backbone interactions and transient secondary structures, while being computationally efficient enough to model liquid-liquid phase separation. This new model fills a critical gap in the existing capability in computer simulation of IDP phase separation.
Chen and team created HyRes simulations to demonstrate for the first time what governs the condensate stability of two important IDPs.
“I actually did not anticipate that it could do such a good job at describing phase separation because it’s a really difficult phenomenon to simulate,” Chen says. “We demonstrated that this model is accurate enough to start looking at the impacts of even a single mutation or residual structures in the phase separation.”
The researchers’ HyRes-GPU provides an innovative simulation tool for studying the molecular mechanisms of phase separation. The ultimate goal is to develop therapeutic strategies in the treatment of diseases associated with disordered proteins.
“This is really the significance of this work,” Chen says. “Important biological processes are believed to occur through phase separation. So, if we can understand better what controls this process, that knowledge will be really powerful, if not essential, for us to think about controlling phase separation for various scientific and engineering purposes. This will help us understand the type of intervention that will be required to achieve therapeutic effects.”
Chen says the next step is to apply what his team has learned to larger-scale simulations of more complex biomolecular mixtures.
“Shanlong is now working on constructing a similar model for nucleic acids because phase separation often involves both disordered proteins and nucleic acids,” he says. “We want to be able to describe both key components, and that would allow us to look at many more systems.”
More information: Yumeng Zhang et al, Toward Accurate Simulation of Coupling between Protein Secondary Structure and Phase Separation, Journal of the American Chemical Society (2023). DOI: 10.1021/jacs.3c09195
The hydrogel fibers printed on the glass are covered by partially crosslinked dangling chains, exhibiting great affinity toward water molecules. They act like the grooves on the integuments of lizards to collect droplets and their surface could form a hydration layer to mimic the mucus on the catfish skin to make droplet movement more smooth and quick. Credit: Science China Press
The water in the air originates from both natural and forced evaporation, with condensation being the final and crucial step in water harvesting. Condensation involves nucleation, growth, and shedding of water droplets, which are then collected.
However, uncontrollable growth of condensed droplets leading to surface flooding is a pressing challenge due to insufficient driving forces, posing a threat to sustainable condensation.
A study, led by Prof. Jiuhui Qu, Dr. Qinghua Ji, and Dr. Wei Zhang from Tsinghua University, focuses on addressing water scarcity by exploring atmospheric water harvesting. The work is published in the journal National Science Review.
To expedite this process and achieve orderly and rapid droplet shedding from the condensing surface, the team took inspiration from nature. They observed that the Australian thorny devil efficiently spread droplets, such as rains, dews, and pond water, from its scales to capillary channels between the scales, eventually connecting to its mouth.
This natural mechanism made water easier to store and consume. Additionally, the team drew inspiration from fish, particularly catfish, which possess an epidermal mucus layer reducing swimming drag and enhancing adaptability to aqueous environments. These insights from nature address the challenges of orderly droplet navigation and low-drag droplet shedding, respectively.
The research team employed hydrogel fibers to create an engineered pattern on glass, incorporating the advantageous features of both lizards and catfish.
The hydrogel fiber is an interpenetrated network of sodium alginate and polyvinyl alcohol with a partially polymerized surface and arch structure. The surface, adorned with branched –OH and –COOH chains, exhibits a strong affinity for water molecules.
This affinity, coupled with the arch structure, provides sufficient driving force for droplets to move from the condensing substrate to the hydrogel fiber. Simultaneously, the branched –OH and –COOH chains can retain water molecules even after droplets leave the surface, aiding in the formation of a precursor water film that lubricates droplet sliding.
The fluorescent molecules were first immobilized on the glass as probes. After condensation, droplets re-dissolved the probes and quickly transferred from the glass to the hydrogel fiber and then slid along the fiber quickly. Credit: Science China Press
To observe droplet movement, fluorescent molecules were utilized as probes. The captured trajectories revealed an impressive migration rate, with droplets formed on the glass swiftly pumped to the hydrogel fiber, thereby regenerating the condensing sites.
The success lies in the concurrent application of chemical wetting gradients and the Laplace pressure difference across the hydrogel fiber and the glass. The pumping effect resulted in a reduction of over 40% in the energy of the droplet-condensing surface system, acting as the driving force source. “This is similar to the directional water dispersion over the integuments of lizards,” Professor Qu notes.
The researchers also observed distinctions in the movement of water on the hydrogel fiber surface compared to that on glass. On the glass, droplets advanced as a cohesive unit with successive formation of new advancing angles, resulting in complete mixing of fluorescent probes within the droplet during advancement.
In contrast, droplet sliding on the hydrogel fiber surface exhibited a layered behavior. The inner layer of water bonded to the hydrogel surface, while the outer layer slid without direct contact with the hydrogel surface.
“The dangling chains over the hydrogel surface act like the mucus layer of the catfish, lubricating the friction between the droplets and the condensing surface,” explains Dr. Ji.
This engineered hydrogel fiber pattern increased the condensation rate by 85.9% without requiring external energy input. Moreover, it was successfully applied to enhance the water collection rate of solar evaporative water purification by 109%.
This study not only provides insights into natural phenomena but also marks a novel attempt to manipulate droplet movement for condensation. The findings lay the foundation for future endeavors in discovering phenomena and translating theories into practical applications.
More information: Wei Zhang et al, Pumping and sliding of droplets steered by a hydrogel pattern for atmospheric water harvesting, National Science Review (2023). DOI: 10.1093/nsr/nwad334
A Cu-organic interface constructed by in situ reconstruction of Cu phthalocyanine can direct the selectivity of CO electrolysis to a specific multicarbon product, with an acetate Faradaic efficiency (FE) as high as 84.2 %, a record acetate partial current density of 605 mA cm−2, and an acetate yield up to 63.4 %. The impressive acetate selectivity is ascribed to the favorable reaction microenvironment created by the Cu-organic interface.
Alkaline CO2 electrolysis can produce multicarbon (C2+) products such as ethylene and acetate, yet suffers from low CO2 utilization efficiency.
Tandem electrolysis, which connects solid oxide or acidic CO2 electrolysis to CO and alkaline CO electrolysis to C2+ products in sequential electrolyzers, is a carbon-efficient route. However, to date, CO electrolysis generally shows high current density and selectivity for C2+ products, but selective generation of a specific C2+ product is still challenging.
Recently, a research team led by Profs. Wang Guoxiong and Gao Dunfeng from the Dalian Institute of Chemical Physics (DICP) of the Chinese Academy of Sciences (CAS) has proposed a new strategy by constructing metal-organic interfaces for CO electrolysis to acetate with high selectivity.
The researchers tuned the reaction microenvironments surrounding catalytically active sites by constructing Cu-organic interfaces through in-situ electrochemical reconstruction of molecular Cu complexes. Benefiting from the favorable reaction microenvironment, they achieved good catalytic performance for CO electrolysis to acetate, in terms of current density, Faradaic efficiency, and yield.
With a copper phthalocyanine (CuPc) electrode measured in a home-made alkaline membrane electrode assembly (MEA) electrolyzer, they obtained an acetate Faradaic efficiency as high as 84.2% and an acetate carbon selectivity of 92.1% at 500 mA cm-2. The maximum acetate partial current density and formation rate reached 605 mA cm-2 and 0.38 mmol min-1, respectively, translating into an acetate yield as high as 63.4%.
The Cu-organic interface created a favorable reaction microenvironment that enhanced *CO adsorption, lowered the energy barrier for C-C coupling, and facilitated the formation of CH3COOH over other multicarbon products, thus rationalizing the selective acetate production.
“Our study highlights the potential of constructing metal-organic interfaces for tailoring reaction microenvironments for highly selective production of a specific C2+ product from CO electrolysis,” said Prof. Gao.
More information: Youwen Rong et al, Directing the Selectivity of CO Electrolysis to Acetate by Constructing Metal‐Organic Interfaces, Angewandte Chemie International Edition (2023). DOI: 10.1002/anie.202309893
Photographs of the textile samples pegged on the wire fences submerged in the flumes. A – sports vest squares, B & C – fleece squares, D & E – carpet squares. Credit: Forensic Science International (2023). DOI: 10.1016/j.forsciint.2023.111818
Forensic fibers can survive underwater for much longer than previously thought—which could help criminal investigators uncover vital evidence.
New research led by Staffordshire University’s Centre for Crime, Justice and Security has found that fiber evidence can survive on fabrics underwater for several weeks.
Claire Gwinnett, Professor of Forensic and Environmental Science, explained, “Evidence, such as weapons and victim’s bodies, are often found in aquatic environments including rivers and lakes.”
“However, if items have been submerged in water for more than seven days then many forensic examiners believe that any valuable trace evidence will be gone and won’t seek it out.”
To date, very few studies have investigated fiber persistence on fabrics submerged underwater. The dynamic nature of aquatic environments mean that the studies are difficult to conduct in situ and variables, such as water flow rate, are not possible to control.
The Forensic Fiber Freshwater (3F) project was conducted in partnership with Lunz Mesocosm Infrastructure (LMI), WasserCluster Lunz, the University of Vienna and Italy’s University of Milano-Bicocca.
This study used artificial streams, known as mesocosms, to investigate the persistence rate of polyester fibers on different fabric types over a four-week exposure time.
Usually used for ecological research, this is the first time that mesocosms have been employed to look at forensic evidence.
Two flow velocities, high and low, were used on three textiles: woolen/nylon mix carpet, 100% polyester fleece, and 95% polyester/5% elastane sports vest.
Initial loss rates were highest for the first hour of submergence for the carpet, fleece and sports vest. However, persistence rates remained mostly constant after 24 hours for all textiles and the two flow rates used did not significantly affect fiber persistence.
Ph.D. researcher Afsané Kruszelnicki said, “It would be expected that a higher flow rate would have a lower number of retained fibers compared to a lower flow rate, yet no significant difference was seen in all but one condition.”
“Even after four weeks, the lowest percentage of remaining fibers was 33.4%. This clearly indicates that it is extremely valuable to search for fiber evidence even after a long exposure time.”
Professor Gwinnett said, “Our findings could change how police direct investigations and help to uncover forensic evidence that was previously thought to be lost. We hope this will help investigators to identify more suspects and ultimately lead to more convictions.”
“The study also highlights the benefits of using mesocosms which mimic realistic aquatic environments in a controlled setting. This is the first time that mesocosms have been used to look at forensic evidence and we hope it will pave the way for further studies to investigate different types of trace evidence such as gunshot residue, pollen, fingerprints or DNA.”
Dr. Katrin Attermeyer, coordinator of the stream mesocosms in Lunz am See and aquatic microbial ecologist at WasserCluster Lunz and the University of Vienna, added, “This interdisciplinary collaboration between forensic scientists and aquatic ecologists has not only provided insights into other sciences, but has also shown that mesocosms, traditionally used to answer ecological questions, are a valuable asset to other research areas such as forensic sciences.”
The findings are published in the journal Forensic Science International.
More information: Afsané Kruszelnicki et al, An investigation into the use of riverine mesocosms to analyse the effect of flow velocity and recipient textiles on forensic fibre persistence studies, Forensic Science International (2023). DOI: 10.1016/j.forsciint.2023.111818
Exploring the best condition for a black box function by GPT or Bayesian optimization. The solid line represents the mean of the best value obtained in three independent trials; the semitransparent filled range represents the standard deviation; each raw trial is indicated by a semitransparent line. Credit: Science and Technology of Advanced Materials: Methods (2023). DOI: 10.1080/27660400.2023.2260300
GPT-4, the latest version of the artificial intelligence system from OpenAI, the developers of Chat-GPT, demonstrates considerable usefulness in tackling chemistry challenges, but still has significant weaknesses. “It has a notable understanding of chemistry, suggesting it can predict and propose experimental results in ways akin to human thought processes,” says chemist Kan Hatakeyama-Sato, at the Tokyo Institute of Technology.
GPT-4, which stands for Generative Pre-trained Transformer 4, belongs to a category of artificial intelligence systems known as large language models. These can gather and analyze vast quantities of information in search of solutions to challenges set by users. One advance for GPT-4 is that it can use information in the form of images in addition to text.
Although the specific datasets used for training GPT-4 have not been disclosed by its developers, it has clearly learned a significant amount of detailed chemistry knowledge. To analyze its capabilities, the researchers set the system a series of chemical tasks focused on organic chemistry—the chemistry of carbon-based compounds. These covered basic chemical theory, the handling of molecular data, predicting the properties of chemicals, the outcome of chemical processes and proposing new chemical procedures.
The results of the investigation were varied, revealing both strengths and significant limitations. GPT-4 displayed a good understanding of general textbook-level knowledge in organic chemistry. It was weak, however, when set tasks dealing with specialized content or unique methods for making specific organic compounds. It displayed only partial efficiency in interpreting chemical structures and converting them into a standard notation. One interesting feat was its ability to make accurate predictions for the properties of compounds that it had not specifically been trained on. Overall, it was able to outperform some existing computational algorithms, but fell short against others.
“The results indicate that GPT-4 can tackle a wide range of tasks in chemical research, spanning from textbook-level knowledge to addressing untrained problems and optimizing multiple variables,” says Hatakeyama-Sato. “Inevitably, its performance relies heavily on the quality and quantity of its training data, and there is much room for improvement in its inference capabilities.”
The researchers emphasize that their work was only a preliminary investigation, and that future research should broaden the scope of the trials and dig deeper into the performance of GPT-4 in more diverse research scenarios.
They also hope to develop their own large language models specializing in chemistry and explore their integration with existing techniques.
“In the meantime, researchers should certainly consider applying GPT-4 to chemical challenges, possibly using hybrid methods that include existing specialized techniques,” Hatakeyama-Sato concludes.
More information: Kan Hatakeyama-Sato et al, Prompt engineering of GPT-4 for chemical research: what can/cannot be done?, Science and Technology of Advanced Materials: Methods (2023). DOI: 10.1080/27660400.2023.2260300
Cocoa pods, like this one with parts of the husk removed for analyses, could be a useful starting material for flame retardants. Credit: Dimitris Charalampopoulos
As Halloween approaches, so too does the anticipation of a trick-or-treating stash filled with fun-sized chocolate candy bars. But to satisfy our collective craving for this indulgence, millions of cocoa pods are harvested annually. While the beans and pulp go to make chocolate, their husks are thrown away. Now, researchers reporting in ACS Sustainable Chemistry & Engineering show that cocoa pod husks could be a useful starting material for flame retardants.
It’s estimated that about 24 million tons of leftover cocoa pod husks are produced yearly. Waste husks have been explored as a source of carbohydrates and sugars, but they also contain lignin, a tough lipid polymer found in many woody plants. And lignin could be a renewable replacement for some substances typically derived from petroleum, such as flame retardants.
While most methods to produce lignin have centered on hardwood trees, some scientists have processed other plant materials that would otherwise go to waste, such as rice husks and pomegranate peels. So, Nicholas J. Westwood and coworkers wanted to see if high-quality lignin could be extracted from cocoa pod husks and determine whether it has the potential to make valuable, practical materials.
The researchers obtained cocoa husks and milled them into a powder. After rinsing to remove fatty residues, they boiled the powdered husks in a mixture of butanol and acid, a standard lignin extraction method called the butanosolv process. They next confirmed the isolated lignin’s quality and high purity, finding no evidence of carbohydrates or other contaminants.
Then, over the course of three chemical steps, the team modified the pure lignin biopolymer to have flame-retardant properties. They attached 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide, which is a fire suppressant molecule called DOPO, into the backbone of the lignin polymer.
In experiments, when the modified lignin was heated, it charred—but did not burn up—a sign that it could act as a flame retardant. The researchers recognize that human safety tests are important and plan to conduct them after the next phase of testing. In the future, the researchers say they will optimize the properties of their cocoa pod husk-based flame-retardant materials.
More information: Daniel J. Davidson et al, Organosolv Pretreatment of Cocoa Pod Husks: Isolation, Analysis, and Use of Lignin from an Abundant Waste Product, ACS Sustainable Chemistry & Engineering (2023). DOI: 10.1021/acssuschemeng.2c03670
