Article
Water Resources
Zhihao Jiang, Pejman Tahmasebi, Zhiqiang Mao
Summary: In this study, a deep learning surrogate model was developed to predict time-dependent multiphase flow in a 2D geological system, achieving accurate results with less training data. By combining residual U-net and autoregressive strategy, the surrogate model showed improved prediction performance, providing effective measures for uncertainty analysis in subsurface systems.
ADVANCES IN WATER RESOURCES
(2021)
Article
Chemistry, Multidisciplinary
Morten O. Loehr, Nathan W. Luedtke
Summary: This article presents a dual enhancement strategy for nucleic acid-templated reactions, which allows for dynamic imaging of chemically modified nucleic acids in living cells.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2022)
Article
Energy & Fuels
Marco Avila, Beatrice Kawas, David Frederick Fletcher, Martine Poux, Catherine Xuereb, Joelle Aubin
Summary: The continuous oscillatory baffled reactor (OBR) is a type of tubular reactor that intensifies heat and mass transfer through the interaction of oscillatory flow with internal baffles. While OBRs have been applied in various industrial sectors, there are limitations in terms of operating conditions and potential applications.
CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION
(2022)
Article
Energy & Fuels
Laura L. Trinkies, Derrick Ng, Zongli Xie, Christian H. Hornung, Manfred Kraut, Roland Dittmeyer
Summary: In order to shift from centralized to decentralized production of hydrogen peroxide (H2O2) and enhance safety, researchers are investigating the direct synthesis of H2O2 as an alternative to the conventional anthraquinone auto-oxidation process. This study presents a method to coat Pd/TiO2 catalyst onto additively manufactured steel substrates using a simple washcoating process. The resulting structured catalysts exhibit high activity and stability, making them suitable for the investigated process.
CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION
(2023)
Article
Mechanics
Sofia Angriman, Amelie Ferran, Florencia Zapata, Pablo J. Cobelli, Martin Obligado, Pablo D. Mininni
Summary: This study investigates the three-dimensional clustering of velocity stagnation points, vorticity nulls, and inertial particles in turbulent flows with different large-scale flow geometries by combining direct numerical simulations and particle tracking velocimetry. The results show that although the flows have different topologies in terms of null clustering, the behavior of particles is similar in all cases, indicating the clustering of Taylor-scale neutrally buoyant particles as inertial particles.
JOURNAL OF FLUID MECHANICS
(2022)
Editorial Material
Multidisciplinary Sciences
Deepak Adhikari, John Carroll
Summary: To ensure the health of the next generation and prevent unwanted mutations, egg cells have discovered a mechanism to avoid damage caused by harmful reactive oxygen species. The production of reactive oxygen species is minimized in oocytes.
Article
Mechanics
Arash Hajisharifi, Cristian Marchioli, Alfredo Soldati
Summary: This study investigates the capture and evolution of sub-Kolmogorov particles at the interface of deformable drops in turbulent flow. The results show that excluded-volume interactions play an important role in trapping the particles in highly convex regions of the interface.
JOURNAL OF FLUID MECHANICS
(2022)
Article
Electrochemistry
Michiel Vranckaert, Hannes P. L. Gemoets, Ruben Dangreau, Koen Van Aken, Tom Breugelmans, Jonas Hereijgers
Summary: Synthetic organic electrochemistry has gained attention due to its environmental impact and energy demand advantages. Researchers propose a novel electrochemical reactor concept that improves mass transfer through oscillatory flow regime and conductive pillar field electrodes.
ELECTROCHIMICA ACTA
(2022)
Article
Computer Science, Interdisciplinary Applications
Yat Tin Chow, Wing Tat Leung, Ali Pakzad
Summary: We propose, analyze, and test a novel continuous data assimilation two-phase flow algorithm for reservoir simulation. We show that the solutions of the algorithm, constructed using coarse mesh observations, converge at an exponential rate in time to the corresponding exact reference solution of the two-phase model. Numerical computations demonstrate the effectiveness of this approach, including variants with data on sub-domains, and synchronization achieved for data collected from a small fraction of the domain.
JOURNAL OF COMPUTATIONAL PHYSICS
(2022)
Article
Mathematics, Applied
Zhenming Wang, Linlin Tian, Jun Zhu, Ning Zhao
Summary: In this paper, a hybrid unequal-sized weighted essentially non-oscillatory (US-WENO) scheme is developed to reduce the computational cost. The proposed hybridization strategy can automatically and efficiently identify the troubled cells, and does not contain artificial parameters. Numerical experiments show that the proposed hybrid method can inherit all the features of the existing US-WENO scheme while improving its computational efficiency.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2023)
Article
Mechanics
Fryderyk Wilczynski, Christopher J. Davies, Christopher A. Jones
Summary: This study develops and analyzes a continuum model of two-phase slurry dynamics in planetary cores. The results suggest that a pure iron slurry F-layer in Earth's core would contain a mean solid fraction of at most 5%.
JOURNAL OF FLUID MECHANICS
(2023)
Article
Engineering, Environmental
Yiran Cao, Natan Padoin, Cintia Soares, Timothy Noe
Summary: A comprehensive understanding of the underlying phenomena is crucial for the design and analysis of chemical reactors for multiphase electro-organic transformations. The study shows that operating at high concentrations of rate-limiting species is beneficial, but excessively high concentrations may not improve mass transfer and current/voltage relation. Keeping an internal:external phase electrical conductivity ratio > 1, working at lower velocities, and operating at higher cell potentials contribute to improved reactor performance.
CHEMICAL ENGINEERING JOURNAL
(2022)
Article
Mechanics
Linfeng Piao, Hyungmin Park
Summary: This study experimentally investigates the interfacial instabilities in a cylindrical container oscillating about its axis with two immiscible liquids, oil and water. The thresholds for the onset of different instabilities responsible for each regime are presented by the amplitude and frequency of rotation, with viscosity playing an important role in shaping the boundary of SD and multiple-droplet regimes.
JOURNAL OF FLUID MECHANICS
(2021)
Article
Chemistry, Applied
Shuang-Shuang Long, Qing Luo, Bin-Bin Yuan, Shu-Qin Gao, Xi-Feng Zou, Ke Zeng, Fei Deng, Ying-Wu Lin
Summary: In this study, a fluorescent probe CP-1 was designed and synthesized to target organelles, which is capable of specifically recognizing and detecting Cys. The probe also exhibits the capability of targeting lipid droplets. Experimental results demonstrate that CP-1 can be used for real-time tracking of Cys in living cells, making it valuable for biomedical applications.
Article
Mechanics
Qiang He, Weifeng Huang, Yuan Yin, Yang Hu, Decai Li
Summary: In this paper, a lattice Boltzmann model with dynamic grid refinement is proposed for simulating immiscible ternary flows. The model is able to conserve the total mass and preserve the volume of each phase, while avoiding the presence of unphysical fluids by introducing a source term into the interface capturing equation. The accuracy and applicability of the model are evaluated through simulations of several typical problems.
Article
Chemistry, Applied
Joshua L. Lansford, Klavs F. Jensen, Brian C. Barnes
Summary: Recent advances in machine learning have allowed for the application of methodologies developed for large datasets to small experimental datasets in chemical systems. By using a data-based approach to transfer learning, a pre-trained model can be fine-tuned on a portion of the experimental data to enable extrapolation outside of the training domain. However, very small experimental datasets require a physics-informed transfer learning strategy, which was demonstrated by training a directed-message passing neural network (D-MPNN) model on interpolated vapor pressures and achieving comparable accuracy to experiments on an out-of-sample test set of energetic molecule vapor pressures.
PROPELLANTS EXPLOSIVES PYROTECHNICS
(2023)
Article
Chemistry, Multidisciplinary
Dylan J. Walsh, Timo N. Schneider, Bradley D. Olsen, Klavs F. Jensen
Summary: This paper presents a design of a versatile, uniform light platform for photochemistry to improve the performance and reproducibility of high throughput experiments. The design is based on the development of an open-source ray tracing light simulation package and is experimentally validated using radiometry. The usefulness of the platform is demonstrated through its application in photoinduced electron transfer-reversible addition-fragmentation chain transfer polymerization of methyl acrylate.
REACTION CHEMISTRY & ENGINEERING
(2023)
Article
Materials Science, Multidisciplinary
Robert W. Epps, Fernando Delgado-Licona, Hyeyeon Yang, Taekhoon Kim, Amanda A. Volk, Suyong Han, Shinae Jun, Milad Abolhasani
Summary: This work presents a universal flow chemistry framework for accelerated studies of heavy metal-free quantum dots (QDs) with multi-stage chemistries. By introducing flexible time- and temperature-to-distance transformation using modular fluidic blocks, a high-speed synthetic route of InP QDs with the highest reported absorption peak to valley ratio is unveiled.
ADVANCED MATERIALS TECHNOLOGIES
(2023)
Article
Materials Science, Multidisciplinary
Milad Abolhasani, Keith A. Brown
Summary: In the past five years, artificial intelligence (AI) has made significant advancements in various aspects of daily life, such as health, transportation, and the digital world, by utilizing data. Inspired by these success stories, materials researchers have started to incorporate AI into experimental materials science to accelerate materials discovery and development. This article reviews the role of AI in experimental materials science and summarizes the key aspects and challenges of autonomous experimentation discussed in each contributed article.
Article
Materials Science, Multidisciplinary
Xiting Peng, Xiaonan Wang, Keith A. Brown, Milad Abolhasani
Summary: The contradiction between the importance of materials to modern society and their slow development process has led to the emergence of intelligent laboratories, which integrate high-throughput experimentation, automation, theoretical computing, and artificial intelligence. These laboratories can autonomously carry out designed experiments and make scientific discoveries. This article presents the basic concepts and foundations of this new research paradigm, showcases typical application scenarios through case studies, and envisions a collaborative human-machine meta laboratory in the future.
Article
Chemistry, Multidisciplinary
Dylan J. Walsh, Weizhong Zou, Ludwig Schneider, Reid Mello, Michael E. Deagen, Joshua Mysona, Tzyy-Shyang Lin, Juan J. de Pablo, Klavs F. Jensen, Debra J. Audus, Bradley D. Olsen
Summary: This article presents the design of a general material data model that supports the application of polymer materials in nearly every aspect of modern life.
ACS CENTRAL SCIENCE
(2023)
Article
Chemistry, Multidisciplinary
Christian P. Haas, Maximilian Luebbesmeyer, Edward H. Jin, Matthew A. McDonald, Brent A. Koscher, Nicolas Guimond, Laura Di Rocco, Henning Kayser, Samuel Leweke, Sebastian Niedenfu, Rachel Nicholls, Emily Greeves, David M. Barber, Julius Hillenbrand, Giulio Volpin, Klavs F. Jensen
Summary: This study introduces an open-source Python project called MOCCA for the analysis of HPLC-DAD raw data, showcasing its broad applicability in data analysis. MOCCA features an automated peak deconvolution routine that can identify known signals and distinguish overlapped signals from unexpected impurities or side products. By releasing MOCCA as a Python package, the researchers aim to foster an open-source community project, advancing its scope and capabilities.
ACS CENTRAL SCIENCE
(2023)
Article
Energy & Fuels
Hamed Morshedian, Milad Abolhasani
Summary: The photostability of colloidal quantum dots (QDs) is crucial for their long-term applicability in energy and chemical technologies. However, current photostability studies are sensitive to experimental conditions, lack mechanistic understanding, and are time, material, and labor-intensive. In this study, an automated microfluidic platform is introduced for accelerated photostability studies of colloidal QDs, which is 3.5x faster and 100x more material efficient compared to conventional flask-based studies. The microfluidic strategy provides real-time access to the optical properties of QDs during the photostability experiments, shedding light on the complex and multifaceted photodegradation phenomena of colloidal QDs and demonstrating the unique advantages of microfluidic strategies for improving and accelerating QD photostability studies.
Article
Chemistry, Multidisciplinary
Bradley A. Davis, Jan Genzer, Kirill Efimenko, Milad Abolhasani
Summary: This study presents a versatile network-supported palladium catalyst for continuous synthesis of complex organic compounds. By using a hybrid polymer, the catalytic system achieves optimized performance in the Suzuki-Miyaura cross-coupling and nitroarene hydrogenation reactions. The system shows high activity, mechanical stability, and reusability, with improved reaction yields and environmentally-friendly solvent usage, making it suitable for industrial applications.
Article
Chemistry, Multidisciplinary
Karthik Sankaranarayanan, Klavs F. Jensen
Summary: Chemoenzymatic synthesis methods combine organic and enzyme chemistry to efficiently synthesize small molecules. A multistep retrosynthesis search algorithm is presented in this study to facilitate the chemoenzymatic synthesis of various compounds. The algorithm uses a synthesis planner and a biocatalytic reaction database to identify enzyme-catalyzed reactions and plan synthetic routes for pharmaceutical, specialty, and commodity chemicals. The approach successfully plans chemoenzymatic routes for different compounds and proposes alternative pathways as well.
Article
Chemistry, Multidisciplinary
Aniket P. Udepurkar, Kakasaheb Y. Nandiwale, Klavs F. Jensen, Simon Kuhn
Summary: A new ultrasonic microreactor has been developed to overcome the limitations of reactor clogging and yield reduction caused by the presence of solids in flow photochemical reactions. The performance of the ultrasonic photochemical microreactor is evaluated based on the residence time distribution of liquid and solid, as well as the absorbed photon flux in the reactor. The solid-handling capability of the microreactor is demonstrated by a silyl radical-mediated metallaphotoredox cross-electrophile coupling using a solid base as a reagent.
REACTION CHEMISTRY & ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Christian P. Haas, Maximilian Luebbesmeyer, Edward H. Jin, Matthew A. McDonald, Brent A. Koscher, Nicolas Guimond, Laura Di Rocco, Henning Kayser, Samuel Leweke, Sebastian Niedenfuehr, Rachel Nicholls, Emily Greeves, David M. Barber, Julius Hillenbrand, Giulio Volpin, Klavs F. Jensen
Summary: This paper presents an open-source Python project called MOCCA for the analysis of HPLC-DAD raw data. MOCCA provides a comprehensive set of data analysis features, including an automated peak deconvolution routine. The authors highlight the broad applicability of MOCCA in various studies and envision its further development as an open-source community project.
ACS CENTRAL SCIENCE
(2023)
Article
Automation & Control Systems
Fernando Delgado-Licona, Milad Abolhasani
Summary: The integration of disruptive physical and digital technologies in the form of self-driving labs, which include robotics, additive manufacturing, reaction miniaturization, and artificial intelligence, has the potential to greatly accelerate materials and molecular discovery. By using autonomous robotic experimentation workflows, self-driving labs can access a larger part of the chemical universe and reduce the time-to-solution through iterative hypothesis formulation, intelligent experiment selection, and automated testing. This perspective article discusses the required hardware and software technological infrastructure to unlock the true potential of self-driving labs, including process intensification and digitalization strategies.
ADVANCED INTELLIGENT SYSTEMS
(2023)
Article
Chemistry, Physical
Fazel Bateni, Sina Sadeghi, Negin Orouji, Jeffrey A. Bennett, Venkat S. Punati, Christine Stark, Junyu Wang, Michael C. Rosko, Ou Chen, Felix N. Castellano, Kristofer G. Reyes, Milad Abolhasani
Summary: This study introduces Smart Dope, a self-driving fluidic lab technology, for accelerated synthesis and autonomous optimization of lead halide perovskite quantum dots. Through the use of a high-pressure gas-liquid segmented flow format, Smart Dope successfully synthesizes multi-cation-doped CsPbCl3 quantum dots and autonomously discovers the optimal synthetic route with a photoluminescence quantum yield of 158%.
ADVANCED ENERGY MATERIALS
(2023)
Article
Materials Science, Multidisciplinary
Tonghui Wang, Ruipeng Li, Hossein Ardekani, Lucia Serrano-Lujan, Jiantao Wang, Mahdi Ramezani, Ryan Wilmington, Mihirsinh Chauhan, Robert W. Epps, Kasra Darabi, Boyu Guo, Dali Sun, Milad Abolhasani, Kenan Gundogdu, Aram Amassian
Summary: This research demonstrates the use of the RoboMapper platform to accelerate materials research, allowing high-throughput measurements and efficient generation of quantitative structure-property relationships. By constructing QSPR maps for halide perovskites, alloy compositions suitable for perovskite-Si hybrid tandem solar cells with superior stability are identified.
