Chemical Systems Research Articles

Information theoretic clustering for coarse-grained modeling of non-equilibrium gas dynamics

We present a new framework towards the objective of learning coarse-grained models based on the maximum entropy principle. We show that existing methods for assigning clusters using the maximum entropy approach are heuristic or sub-optimal. We propose a machine learning framework informed by rate-distortion theory to learn optimal cluster assignments, aiming to improve the effectiveness of the coarse-graining process. Our approach involves transforming the discrete optimization problem into a probabilistic and continuous form. Our inverse modeling approach involves the development of a fully differentiable, adjoint-driven dynamics solver for use with gradient-based optimization techniques. The entire framework is end-to-end differentiable, facilitating backward pass gradient computation to flow through the ODE solve and probabilistic coarse-graining to train a classifier. We demonstrate application in the evolution of particle quantum states in non-equilibrium conditions. The high dimensionality of these equations poses a significant challenge for efficient and accurate computations, even for seemingly simple chemical systems. Training is performed using a loss function informed by rate-distortion theory to cluster the rovibrational states of some molecule, effectively forming a reduced order model of the master equations. We also introduce variable transformations along with several ways of improving the tractability of the training process. These allow the framework to process the extremely high-dimensional discrete optimization problem successfully. Our method is general, and to demonstrate the effectiveness in a controlled setting, we apply it to a one-dimensional Gaussian source quantization problem for which an analytical solution is known, followed by the problem of isothermal relaxation of a ▪ system with O(104) degrees of freedom.

Self-Consistent Field Approach for the Variational Quantum Eigensolver: Orbital Optimization Goes Adaptive.

We present a self-consistent field (SCF) approach within the adaptive derivative-assembled problem-tailored ansatz variational quantum eigensolver (ADAPT-VQE) framework for efficient quantum simulations of chemical systems on near-term quantum computers. To this end, our ADAPT-VQE-SCF approach combines the idea of generating an ansatz with a small number of parameters, resulting in shallow-depth quantum circuits with a direct minimization of an energy expression that is correct to second order with respect to changes in the molecular orbital basis. Our numerical analysis, including calculations for the transition-metal complex ferrocene [Fe (C5H5)2], indicates that convergence in the self-consistent orbital optimization loop can be reached without a considerable increase in the number of two-qubit gates in the quantum circuit by comparison to a VQE optimization in the initial molecular orbital basis. Moreover, the orbital optimization can be carried out simultaneously within each iteration of the ADAPT-VQE cycle. ADAPT-VQE-SCF thus allows us to implement a routine analogous to the complete active space SCF, a cornerstone of state-of-the-art computational chemistry, in a hardware-efficient manner on near-term quantum computers. Hence, ADAPT-VQE-SCF paves the way toward a paradigm shift for quantitative quantum-chemistry simulations on quantum computers by requiring fewer qubits and opening up for the use of large and flexible atomic orbital basis sets in contrast to earlier methods that are predominantly based on the idea of full active spaces with minimal basis sets.

Syntheses, Crystal Structures, and Electronic Structures of Quaternary Group IV-Selenide Semiconductors.

Early transition-metal chalcogenides have garnered recent attention for their optoelectronic properties for solar energy conversion. Herein, the first Zr-/Hf-chalcogenides with a main group cation, Ba9Hf3Sn2Se19 (1) and Ba8Zr2SnSe13(Se2) (2), have been synthesized. The structure of 1 is formed from isolated SnSe44- tetrahedra and distorted HfSe6 octahedra. The latter condense via face-sharing trimeric motifs that are further vertex-bridged into chains of 1∞[Hf(1)2Hf(2)Se11]10-. The structure of 2 is comprised of SnSe44- tetrahedra, Se22- dimers, and face-sharing dimers of distorted ZrSe6 octahedra. These represent the first reported examples of Hf-/Zr-chalcogenides exhibiting face-sharing octahedra with relatively short Hf-Hf and Zr-Zr distances. Their preparation in high purity is inhibited by their low thermodynamic stability, with calculations showing small calculated ΔUdec values of +7 and +9 meV atom-1 for 1 and 2, respectively. Diffuse reflectance measurements confirm the semiconducting nature of 1 with an indirect band gap of ∼1.4(1) eV. Electronic structure calculations show that the band gap absorptions arise from transitions between predominantly Se-4p valence bands and mixed Hf-5d/Sn-5p or Zr-4d/Sn-5p conduction bands. Optical absorption coefficients were calculated to be more than ∼105 cm-1 at greater than 1.8 eV. Thus, promising optical properties are demonstrated for solar energy conversion within these synthetically challenging chemical systems.

The variational quantum eigensolver self-consistent field method within a polarizable embedded framework.

We formulate and implement the Variational Quantum Eigensolver Self Consistent Field (VQE-SCF) algorithm in combination with polarizable embedding (PE), thereby extending PE to the regime of quantum computing. We test the resulting algorithm, PE-VQE-SCF, on quantum simulators and demonstrate that the computational stress on the quantum device is only slightly increased in terms of gate counts compared to regular VQE-SCF. On the other hand, no increase in shot noise was observed. We illustrate how PE-VQE-SCF may lead to the modeling of real chemical systems using a simulation of the reaction barrier of the Diels-Alder reaction between furan and ethene as an example.

Using clustering as pre-processing in the framework of signal unmixing for exhaustive exploration of archaeological artefacts in Raman imaging

Analytical chemistry on archaeological material is an essential part of modern archaeological investigations and from year to year, instrumental improvement has made it possible to generate data at a high spatial and temporal frequency. In particular, Raman spectral imaging can be successfully applied in archaeological research by its simplicity of implementation to study past human societies through the analysis of their material remains. This technique makes it possible to simultaneously obtain spatial and spectral information by preserving sample integrity. However, because of the inherent complexity of the samples in Archaeology (e.g. seniority, fragility, lack or full absence of any information about its composition), chemical interpretation can be difficult at first glance. Indeed, specific problems of spectral selectivity related to unexpected chemical compounds could appear due to their state of conservation. Furthermore, detecting minor compounds becomes challenging as major components impose their contributions in the acquired spectra. Therefore, a relevant chemometric approach has been introduced in this context to characterize distinct spectral sources in a Raman imaging dataset of an archaeological specimen – a mosaic fragment. The fragment was unearthed during the Ruscino archaeological dig on the outskirts of Perpignan, France. It dates back to the oppidum period. The aim is to extract selective spectral information from pixel clustering analysis in order to enhance the initial optimisation step within the Multivariate Curve Resolution and Alternating Least-Squares (MCR-ALS) algorithm, a well-known signal unmixing technique. The underlying principle of the MCR-ALS is that the acquired spectra can be expressed as linear combinations of pure spectra of all individual components present in the chemical system under study. Sometimes it can be difficult to obtain the desired results through the algorithm, particularly if initial estimates of spectral or concentration profiles are inaccurate due to complex signals, noise or lack of selectivity, resulting in rank deficiency (i.e. a poor estimation of the total number of pure signals). For this reason, an innovative threshold-based clustering algorithm, combined with multiple Orthogonal Projection Approaches (OPA), has been developed to improve matrix rank investigation and thus the initialisation step of the MCR-ALS approach before optimisation. The effective analysis of Raman imaging data for an archaeological mosaic played a crucial role in uncovering significant chemical information about a particular biogenic material. This insight sheds light on the origins of mortar manufacture during the oppidum period.

Quantized Proportional-Integral Tracking Control for Two-Stage Chemical Reactor Systems

Abstract This paper is concerned with the problem of proportional-integral tracking control of a two-stage chemical reactor system subject to time delays, disturbances, uncertainties, and input quantization. In this work, an improved equivalent-input-disturbance estimator is incorporated into the proportional-integral tracking control system to compensate for the disturbances in the addressed model. Moreover, to minimize the communication congestion in the control networks, the quantized control input signals are considered while designing the controller. Further, a robust stability condition for the addressed system is established in the form of linear matrix inequalities by employing asymmetric Lyapunov–Krasovskii functional and Jensen's integral inequalities. Moreover, in accordance with the derived conditions, the control and observer gain matrices are determined. Finally, a numerical example is provided to demonstrate the validity of the proposed control scheme.

Numerical simulation of two-phase gas-liquid flowthrough horizontal annulus pipe

Chemical, petroleum and nuclear systems are only a few of the industrial processes that utilize gas-liquid flow in annular closed channels. However, concentric horizontal annuli flow patterns have received little attention. The ability to precisely characterize two-phase flow patterns using computational techniques is crucial for the production, transportation, and optimization of designs. This current research aims to establish the accuracy of the computational fluid dynamics (CFD) model in predicting the gas-liquid flow pattern in the concentric annulus pipe and validating the flow pattern of liquid holdup with experimental results from the literature. The simulations were done on a test section of a 12.8 m length pipe with a hydraulic diameter of 0.0168 m using air and water as the working fluids. The volume of fluid (VOF) model in Ansys Fluent based on the Eulerian- Eulerian approach in conjunction with the realizable k-ε turbulence model was used to model the gas-liquid flow pattern, i.e. dispersed bubble, elongated bubble, and slug in a horizontal annulus. A comparison of the model with the experimental high-speed video images shows a reasonable agreement for the flow pattern and liquid holdup data.

Dynamic three-dimensional structures of a metal–organic framework captured with femtosecond serial crystallography

Crystalline systems consisting of small-molecule building blocks have emerged as promising materials with diverse applications. It is of great importance to characterize not only their static structures but also the conversion of their structures in response to external stimuli. Femtosecond time-resolved crystallography has the potential to probe the real-time dynamics of structural transitions, but, thus far, this has not been realized for chemical reactions in non-biological crystals. In this study, we applied time-resolved serial femtosecond crystallography (TR-SFX), a powerful technique for visualizing protein structural dynamics, to a metal–organic framework, consisting of Fe porphyrins and hexazirconium nodes, and elucidated its structural dynamics. The time-resolved electron density maps derived from the TR-SFX data unveil trifurcating structural pathways: coherent oscillatory movements of Zr and Fe atoms, a transient structure with the Fe porphyrins and Zr6 nodes undergoing doming and disordering movements, respectively, and a vibrationally hot structure with isotropic structural disorder. These findings demonstrate the feasibility of using TR-SFX to study chemical systems.

Preliminary Experimental Study of Herbal Electrolyte for Future Green Battery

Batteries are turned into one of the widely used energy source in our day-to day life which relies on the electrochemical dynamics and interaction among the electrolyte and electrodes. An electrolyte is important component of battery to decide the transfers ions back and forth between two electrodes which results electrical energy generation. However, electrolytes used in the conventional batteries are very hazardous due to release of toxic chemicals in the environment which affects the growth of plants and aquatic life. Therefore, it becomes clear that the chemical electrolyte system could no longer be required in future and design and characterization of alternative electrolyte is very important. The new electrolyte concepts hold the important metrices of any battery, higher safety, lower cost, and better cycle time. In the present paper we have carried out the preliminary investigation of the performance of Herbal electrolyte, which is cheap, non-flammable and insensitive to temperature. The main constituents of the proposed electrolyte are aloe vera gel, garlic cloves and Indian Jadi Buti which makes the electrolyte eco-friendly and non-toxic and does not cause any damage to environment when it disposed of.

A numerical compass for experiment design in chemical kinetics and molecular property estimation

Kinetic process models are widely applied in science and engineering, including atmospheric, physiological and technical chemistry, reactor design, or process optimization. These models rely on numerous kinetic parameters such as reaction rate, diffusion or partitioning coefficients. Determining these properties by experiments can be challenging, especially for multiphase systems, and researchers often face the task of intuitively selecting experimental conditions to obtain insightful results. We developed a numerical compass (NC) method that integrates computational models, global optimization, ensemble methods, and machine learning to identify experimental conditions with the greatest potential to constrain model parameters. The approach is based on the quantification of model output variance in an ensemble of solutions that agree with experimental data. The utility of the NC method is demonstrated for the parameters of a multi-layer model describing the heterogeneous ozonolysis of oleic acid aerosols. We show how neural network surrogate models of the multiphase chemical reaction system can be used to accelerate the application of the NC for a comprehensive mapping and analysis of experimental conditions. The NC can also be applied for uncertainty quantification of quantitative structure–activity relationship (QSAR) models. We show that the uncertainty calculated for molecules that are used to extend training data correlates with the reduction of QSAR model error. The code is openly available as the Julia package KineticCompass.Graphical

Surface Segregation Studies in Ternary Noble Metal Alloys: Comparing DFT and Machine Learning with Experimental Data.

Surface segregation, whereby the surface composition of an alloy differs systematically from the bulk, has historically been hard to study, because it requires experimental and modeling methods that span alloy composition space. In this work, we study surface segregation in catalytically relevant noble and platinum-group metal alloys with a focus on three ternary systems: AgAuCu, AuCuPd, and CuPdPt. We develop a data set of 2478 slabs with those compositions including all three low-index crystallographic orientations relaxed with Density Functional Theory using the PBEsol functional with D3 dispersion corrections. We fine-tune a machine learning model on this data and use the model in a series of 1800 Monte Carlo simulations spanning ternary composition space for each surface orientation and ternary chemical system. The results of these simulations are validated against prior experimental surface segregation data collected using composition spread alloy films for AgAuCu and AuCuPd. Our findings reveal that simulations conducted using the (110) orientation most closely match experimentally observed surface segregation trends, and while predicted trends qualitatively match observation, biases in the PBEsol functional limit numeric accuracy. This study advances understanding of surface segregation and the utility of computational studies and highlights the need for further improvements in simulation accuracy.

Seeing is Believing: Developing Multimodal Metabolic Insights at the Molecular Level.

This outlook explores how two different molecular imaging approaches might be combined to gain insight into dynamic, subcellular metabolic processes. Specifically, we discuss how matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and stimulated Raman scattering (SRS) microscopy, which have significantly pushed the boundaries of imaging metabolic and metabolomic analyses in their own right, could be combined to create comprehensive molecular images. We first briefly summarize the recent advances for each technique. We then explore how one might overcome the inherent limitations of each individual method, by envisioning orthogonal and interchangeable workflows. Additionally, we delve into the potential benefits of adopting a complementary approach that combines both MSI and SRS spectro-microscopy for informing on specific chemical structures through functional-group-specific targets. Ultimately, by integrating the strengths of both imaging modalities, researchers can achieve a more comprehensive understanding of biological and chemical systems, enabling precise metabolic investigations. This synergistic approach holds substantial promise to expand our toolkit for studying metabolites in complex environments.

Voltammetric Detection of Singlet Oxygen Enabled by Nanogap Scanning Electrochemical Microscopy.

Despite the significance of singlet oxygen (1O2) in several biological, chemical, and energy storage systems, its voltammetric reduction at an electrode remains unreported. We address this issue using nanogap scanning electrochemical microscopy (SECM) in substrate-generation/tip-collection mode. Our investigation reveals a reductive process on the SECM tip at -1.0 V (vs Fc+/Fc) during the breakdown of the Li2CO3 substrate in deuterated acetonitrile. Notably, this value is approximately 0.9 V more positive than the reduction potential of triplet oxygen (3O2), consistent with thermodynamic estimates for the energy of the formation of 1O2. This finding holds significant implications for understanding the reaction mechanisms involving 1O2 in nonaqueous media.

Petri Net Model Predictive Control Method for Batch Chemical Systems

In order to address the problem of the real-time scheduling and control of batch chemical systems, this work proposes a model predictive control method based on Petri nets. First, a method is presented to construct a batch chemical system’s timed Petri net model. Second, a control structure is designed to augment the Petri net model to control the valves. This results in timed Petri nets that formally represent the process specifications of a batch chemical system. Third, a model predictive control method is developed to schedule and control timed Petri nets, where a proposed heuristic function is utilized to perform the optimization computation. The model parameters are dynamically adjusted using online data, and both scheduling and valve control instructions are calculated in real time. Finally, a series of experiments is carried out in a beer canning plant to verify the proposed method. According to the experimental results, the scheduling and control problem can be solved in real time, where the online computations can be performed in milliseconds, and the resulting scheduling strategies are optimal or near-optimal.

Comparative analysis of soil quality and enzymatic activities under different tillage based nutrient management practices in soybean–wheat cropping sequence in Vertisols

In the modern era, intensive agricultural practices such as agrochemicals are applied in excessive amounts to enhance agricultural production. However, imbalanced adoption of these chemicals has arisen in the dwindling of agriculture factor productivity and soil quality. To maintain soil fertility and production, these chemical fertilizers must be supplemented with organic inputs. Keeping this in the backdrop, a research trail was established during 2018–19 and 2019–20 years at Research Farm of Agriculture University, Kota, India. The treatment setup was comprised of 5 treatment modules viz., conservation tillage + organic management (CAOM), conservation tillage + chemical management (CACM), conventional tillage + chemical management (CTCM), conventional tillage + organic management (CTOM) and the package of practices (PoPs) with four replications. Results indicated that the highest organic carbon (0.68%), bacterial (29.11 × 107 cfu g−1), fungal (4.77 × 104 cfu g−1), actinomycetes populations (5.67 × 104 cfu g−1), acid phosphatase (44.1 µg g−1 h−1), urease (45.3 µg g−1 h−1) and dehydrogenase (23.3 µg triphenylformazan [TPF] g−1 h−1) activity in soil were found in the treatment of conservation organic system during both the years of study at each soil depth. In contrast to other parameters, the highest system productivity was observed with conservation chemical crop management approaches, with a soybean equivalent yield of 4615 kg ha−1 in a soybean–wheat system of production. Furthermore, the soil quality index (SQI) significantly varied from the lowest score (0.30) at 45–60 cm layer of soil in the package of practices to the highest score (0.92) at 0–15 cm layer of soil with regards to the conservation organic which shows, 206.67 percent enhancement through the soil profile of various crop management practices. The SQI variation from 0–15 to 45–60 cm soil depth was 130.0, 81.08, 60.0, 175.0 and 83.33 percent, respectively, for CAOM, CACM, CTCM, CTOM and PoPs. Amongst, different systems, the highest mean performance was noticed under the conservation organic systems for physical and biological properties. Hence, in line with the salient outcome, we may propose that the conservation chemical system needs to be followed to improve crop productivity, whereas, conservation organic seems a good option for soil health with long-term viability.

Abiotic Small Molecule Inhibitors and Activators of the LasR Quorum Sensing Receptor in Pseudomonas aeruginosa with Potencies Comparable or Surpassing N-Acyl Homoserine Lactones.

The opportunistic pathogen Pseudomonas aeruginosa controls almost 10% of its genome, including myriad virulence genes, via a cell-to-cell chemical communication system called quorum sensing (QS). Small molecules that either inhibit or activate QS in P. aeruginosa represent useful research tools to study the role of this signaling pathway in infection and interrogate its viability as an antivirulence target. However, despite active research in this area over the past 20+ years, there are relatively few synthetic compounds known to strongly inhibit or activate QS in P. aeruginosa. Most reported QS modulators in this pathogen are of low potency or have structural liabilities that limit their application in biologically relevant environments such as mimics of the native N-acyl l-homoserine lactone (AHL) signals. Here, we report the results of a high-throughput screen for abiotic small molecules that target LasR, a key QS regulator in P. aeruginosa. We screened a 25,000-compound library and discovered four new structural classes of abiotic LasR modulators. These compounds include antagonists that surpass the potency of all known AHL-type compounds and mimetics thereof, along with an agonist with potency approaching that of LasR's native ligand. The novel structures of this compound set, along with their anticipated robust physicochemical profiles, underscore their potential value as probe molecules to interrogate the roles of QS in this formidable pathogen.

A Methodology for the Preliminary Design of a High-Efficiency Multistage Plasma Thruster

AbstractSpace electric propulsion represents a class of power-limited systems that utilize the interaction of electromagnetic fields with ionized inert gas propellants to generate thrust. This technology has emerged as a highly fuel-efficient and sustainable alternative to chemical propulsion systems, particularly for satellite constellations. However, the miniaturization potential of EP systems is impeded by certain limitations, necessitating the exploration of novel architectures. The high-efficiency multistage plasma thruster (HEMP-T) stands as a promising contender for stand-alone missions due to its employment of a cusped magnetic-field topology, which effectively mitigates plasma-wall interactions and enhances overall efficiency even at low thrust levels. Despite the growing interest in HEMP-Ts, there is a dearth of comprehensive and streamlined preliminary design procedures for these systems. Prior research has predominantly focused on extensive numerical analyses, neglecting the development of efficient and accessible design tools. To bridge this gap, this paper presents a novel preliminary design tool derived from integrating established analytical models available in the literature. The proposed design tool also incorporates an iterative procedure that refines geometric properties using a 2D magnetostatic solver. Through the application of this tool, a 4 mN HEMP thruster was analyzed. This finally exhibited a specific impulse of approximately 2000s and a good efficiency level of 23%. Also, the results obtained for a 10 mN application align closely with those achieved by other types of EP thrusters.

Comparison of Measurements for Enhanced Diffusion Problem in Chemical Reaction Systems

Comprehensive SummaryThe problem of molecular diffusion in the soup of chemical reactions attracts mounting interest across fields ranging from chemistry to biophysics to material science. Chemical reactions involve bond breakup and formation, whose time scale is typically on the orders of fs to ps, while molecular diffusion occurs at time scales of μs to ms. The two processes are often considered orthogonal, given the vastly different scales. The serial results show that the enzyme's diffusion is enhanced in a substrate‐dependent manner, which was further extended to small molecule reaction systems, challenging this classical paradigm. However, the results from different groups using different techniques do not quantitatively agree, and a general mechanism is yet to be understood. We summarize experimental studies on diffusion problems and seek to reconcile the interpretation with understanding the limits of measurement tools and the chemical nature of reaction systems. Understanding molecular diffusion in chemical reactions will provide fresh thoughts in designing chemical systems such as molecular machines that harvest work at the nanoscale in a controllable manner.

Optimizing Propellant Distribution for Interorbital Transfers

The advent of space exploration missions, especially those aimed at establishing a sustainable presence on the Moon and beyond, necessitates the development of efficient propulsion and mission planning techniques. This study presents a comprehensive analysis of chemical and electric propulsion systems for spacecraft, focusing on optimizing propellant distribution for missions involving transfers from Low-Earth Orbit (LEO) to Geostationary Orbit (GEO) and the Lunar surface. Using mathematical modeling and optimization algorithms, we calculate the delta-v requirements for key mission segments and determine the propellant mass required for each propulsion method. The results highlight the trade-offs between the high thrust of chemical propulsion and the high specific impulse of electric propulsion. An optimization model is developed to minimize the total propellant mass, considering a hybrid approach that leverages the advantages of both propulsion types. This research contributes to the field of aerospace engineering by providing insights into propulsion system selection and mission planning for future exploration missions to the Moon, Mars, and Venus.

Synchronization of corotating spirals anchored to heterogeneities in a chemical system

Synchronization of corotating spirals anchored to heterogeneities in a chemical system