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Enhanced Battolyser stores electricity four times faster than before

A new 3D design for electrodes enables the Battolyser™, a battery and electrolyser in one, to store twice the amount of electricity it could previously hold and do so four times faster. Researchers from Delft University of Technology have detailed their findings in the scientific journal Cell Reports Physical Science. The Battolyser now charges and produces hydrogen at a rate comparable to current electrolysers, all without relying on scarce precious metals. This increased capacity saves both space and costs.

Atmospheric pressure atomic layer deposition for in-channel surface modification of PDMS microfluidic chips

In this paper, we designed a new concept where we deposited PDMS microfluidics devices with nano-layers. Using atmospheric pressure atomic layer deposition, we obtained a high coverage, even at a complex geometry and high aspect ratios. The nano-layers can be used for various purposes, as demonstrated with three case studies in our papers. For example, an increase of the organic solvent resistance (and their wetting property), which enables PDMS microfluidics devices to produce oil-in-water droplets often employed in emerging drug productions. Other positive properties include modifying PDMS devices' surface to evaluate unique responses of cancer cells, and decorate PDMS devices with arrays of functional nanoparticles to elucidate various catalysis in micro-level. Abstract Polydimethylsiloxane (PDMS) is one of the materials of choice for the fabrication of microfluidic chips. However, its broad application is constrained by its incompatibility with common organic solvents and the absence of surface anchoring groups for surface functionalization. Current solutions involving bulk-, ex-situ surface-, and in-situ liquid phase modifications are limited and practically demanding. In this work, we present a simple, novel strategy to deposit a metal oxide nano-layer on the inside of bonded PDMS microfluidic channels using atmospheric pressure atomic layer deposition (AP-ALD). Using three important classes of microfluidic experiments, i.e., (i) the production of micron-sized particles, (ii) the cultivation of biological cells, and (iii) the photocatalytic degradation in continuous flow chemistry, we demonstrate that the metal oxide nano-layer offers a higher resistance against organic solvent swelling, higher hydrophilicity, and a higher degree of further functionalization of the wall. We demonstrate the versatility of the approach by not only depositing SiO x nano-layers, but also TiO x nano-layers, which in the case of the flow chemistry experiment were further functionalized with gold nanoparticles through the use of AP-ALD. This study demonstrates AP-ALD as a tool to broaden the applicability of PDMS devices. Albert Santoso, M. Kristen David, Pouyan E. Boukany, Volkert van Steijn, J. Ruud van Ommen Albert Santoso Read the article

4TU Energy grant for Bijoy Bera for research (with UT) on Magneto-Iono-caloric Heat Pumps

Recently, dr. Bijoy Bera (Interfacial Physis Lab/Transport Phenomena Section) received, together with his collaborator dr. Keerthivasan Rajamani (University of Twente), the 4TU Energy grant, which promotes collaborative efforts among the four technical universities of NL to address the energy issues/future of this country. ChemE News sat down with Bijoy for more info. What is a heat pump? Why does NL need them? What’s wrong with the current heat pumps? A pump is a device where we put (electrical) energy to obtain work. Heat pump is where work (together with heat from a source) is supplied to a device to obtain heat, very useful for efficient heating of households. The heating demand for the built environment in the Netherlands alone is expected to be 333 PJ of energy in 2030. As of 2022, 82% of Dutch households still use natural gas for heating. (Traditional vapor compression system) Heat pumps are being increasingly used in Dutch households (if you ask me, not as much as should be), but the major problem is their efficiency, which tends to hover around 40%-50%. How is your research going to improve the situation? Dr. Rajamani (UT) and I are going to investigate, model and design a new type of heat-pump: Magneto-iono-caloric heat pumps. We plan to use magnetic ionic liquids where low strength magnetic field can be used to bring the melting point of a salt down to below the room temperature. The heat of solidification/crystallization of the salt can then subsequently be used as the heat source of the heat pump, which will lead to higher Carnot efficiency. What is the nature of the collaboration in this project? Keerthi (Dr Rajamani) is an expert in magneto-caloric devices where magnetic fields are applied to change the energy input/output of a system. I will bring my expertise of ionic manipulation of energy interactions in a system. Keerthi and I were chatting about our areas of interest about a year ago, and we realized that by combining these two points of interest, we can come up with something unique! Dr. Bijoy Bera Why is this research important? Will this grant be sufficient in that quest? There is right now a strong direction in the Dutch research landscape to contribute to new forms of energy and how to increase efficiency in processes producing these forms of energy. However, classic thermodynamic processes (such as a heat pump) are often overlooked. This grant is a small but timely incentive for us to start the work, and hopefully our results will inspire colleagues to join us and create a platform for something bigger. Sounds interesting! When can we buy magneto-iono-caloric heat pumps for our houses? Not for a little while, unfortunately! But we are talking about years not decades! And once we can make it, it will open many doors for us, not only for household heating, but for renewed faith in novel energy systems!

Understanding the learning process: machine learning and computational chemistry for hydrogenation

Machine learning is being mentioned all around, but can it be applied to modelling homogeneous catalysis? Researchers from TU Delft together with Janssen Pharmaceuticals published an extensive study accompanied by one of the biggest datasets on rhodium-catalyzed hydrogenation in Chemical Science trying to answer this question. Adarsh Kalikadien Evgeny Pidko For more than half a century, Rhodium-based catalysts have been used to produce chiral molecules via the asymmetric hydrogenation of prochiral olefins. The importance of this transformation was acknowledged by a Nobel prize given to Noyori and Knowles for their contributions in this field. Nowadays, asymmetric hydrogenation catalysts are widely used in the pharmaceutical industry, numerous chiral ligands are available to tackle a wide range of prochiral substrates and the reaction mechanism has been extensively studied. Consequently, one would expect that finding the best catalyst for the asymmetric hydrogenation of a new substrate is a trivial task. Unfortunately, this is not the case and a tedious and costly experimental screening is still needed. Adarsh Kalikadien and Evgeny Pidko from TU Delft together with experts in high-throughput-experimentation, data science and computational chemistry from Janssen Pharmaceutica in Belgium decided to investigate whether a well-trained machine could do the job. To their surprise, the machine was actually not able to learn as much as they expected. The idea was to set up a simple model reaction with a well-known rhodium catalyst. Based on the experimental data generated by the high-throughput experimentation team of Janssen, a computational dataset was built to which multiple machine learning models were applied. “We digitalized the 192 catalyst structures and represented them with features of various levels of complexity for the machine learning models,” says Kalikadien, a PhD student in Pidko’s group. "The interesting thing was that all the simpler models, including the random model, showed similar performances as the expensive variant, which intrigued us. It turned out to be an early indication that the machine was not really learning anything useful.” "One of our conclusions was, when tested more extensively, that for an out-of-domain modeling approach, it doesn't matter what representation you put in”. Nevertheless, although the team was not able to build an accurate model, their study was worth publishing. The publication process went relatively smoothly. “Although the first journal we contacted rejected our submission as too specialized, the high-impact journal Chemical Science saw the value of this work. Not many researchers are interested in just seeing the R2 value of a model and then having no possibility to use it, they are probably interested in an in-depth analysis like ours. So we were able to submit our data, code and even interactive figures there for everyone to use.” At the moment there is a big incentive for publishing negative data in order to help the community to assess the true added value of machine learning, since models trained on mainly positive results tend to become very biased. "We made everything open source," says Kalikadien. "Not only is all the data accessible, but we also offer the code including packages and instructions, so that anyone who is interested can do the same type of research." In this way, they have published one of the largest datasets of a certain type of hydrogenation reaction. What's next? "Our representation of the catalyst wasn't as meaningful for the machine learning models as we had hoped, so we are now looking for a representation that may be less simplified but still as simple as possible," says Kalikadien. "Creating a digital representation of your catalyst should not cost way more money than running the actual experiment, so we are trying to incorporate more information from the reaction mechanism into the model without making it too extensive. A more dynamic and hopefully more informative version of the representation." Read the publication Adarsh Kalikadien, Cecile Valsecchi, Robbert van Putten, Tor Maes, Mikko Muuronen, Natalia Dyubankova, Laurent Lefort and Evgeny A. Pidko

Bipolar membranes for intrinsically stable and scalable CO2 electrolysis

The energy transition requires technology to supply sustainable carbon-based chemicals for hard-to-abate sectors such as long-distance transport and plastic manufacturing. These necessary hydrocarbon chemicals and fuels, responsible for 10-20% of the global greenhouse gas emissions, can be produced sustainably by the electrolysis of captured CO 2 using renewable electricity. Currently, the state-of-the-art CO 2 electrolyzers employ anion exchange membranes (AEMs) to facilitate the transport of hydroxide ions from the cathode to the anode. However, CO 2 is crossing the membrane as well, resulting in a loss of reactant and unfavourable anode conditions which necessitates the use of scarce anode materials. Bipolar membranes (BPMs) offer an alternative that addresses the problem of CO 2 crossover but still requires research to match the product selectivity of AEM-based systems. Our perspective, a collaboration between groups of David Vermaas, Tom Burdyny and Marc Koper, published in Nature Energy, assesses the potential of BPMs for CO 2 electrolysis by looking at CO 2 utilization, energy consumption, and strategies to improve the product selectivity. Abstract CO 2 electrolysis allows the sustainable production of carbon-based fuels and chemicals. However, state-of-the-art CO 2 electrolysers employing anion exchange membranes (AEMs) suffer from (bi)carbonate crossover, causing low CO 2 utilization and limiting anode choices to those based on precious metals. Here we argue that bipolar membranes (BPMs) could become the primary option for intrinsically stable and efficient CO 2 electrolysis without the use of scarce metals. Although both reverse- and forward-bias BPMs can inhibit CO 2 crossover, forward-bias BPMs fail to solve the rare-earth metals requirement at the anode. Unfortunately, reverse-bias BPM systems presently exhibit comparatively lower Faradaic efficiencies and higher cell voltages than AEM-based systems. We argue that these performance challenges can be overcome by focusing research on optimizing the catalyst, reaction microenvironment and alkali cation availability. Furthermore, BPMs can be improved by using thinner layers and a suitable water dissociation catalyst, thus alleviating core remaining challenges in CO 2 electrolysis to bring this technology to the industrial scale. Go to the publication Kostadin Petrov Christel Koopman David Vermaas Tom Burdyny Siddharta Subramanian

Fourteen promising young Delft researchers receive Veni grant

The Dutch Research Council (NWO) has awarded thirteen young TU Delft researchers from the Science (ENW) and Applied and Engineering Sciences (TTW) domains, a Veni grant of up to 320,000 euro.

Events

December

05

e-Refinery lunch lecture | Theoretical studies of the mechanism of C1 and C2 product formation in CO2 electrochemical reduction

     

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