Chemical Systems Research Articles

Comparisons of Different Representative Species Selection Schemes for Reduced-Order Modeling and Chemistry Acceleration of Complex Hydrocarbon Fuels

The simulation of engine combustion processes, such as autoignition, an important process in the co-optimization of fuel-engine design, can be computationally expensive due to the large number of thermo-chemical scalars needed to describe the full chemical system. Yet, the inherent correlations between the different chemical species during oxidation can significantly reduce the complexity of representing this system. One strategy is to select a subset of representative species that accurately captures the combustion process at a fraction of the computational cost of the full system. In this study, we compare the performance of four different techniques to select these species. They include the two-step principal component analysis (PCA) approach, directed relation graphs (DRGs), the global pathway selection (GPS) approach, and the manifold-informed species selection method. A parametric study of the representative species selection is carried out on data from the simulation of homogeneous and perfectly stirred reactors by investigating seven cumulative variances and 47 different cut-off percentages for the two-step PCA, and 65 and 51 thresholds for the DRGs and GPS, respectively. Results show that these selection methods capture key important species that can accurately describe the chemical system and track each stage of oxidation. The two-step PCA is sensitive to the cumulative variance, and DRGs and GPS are sensitive to the choice of target variables. By selecting key representative species and reducing the number of thermo-chemical scalars, these three methods can be used to develop computationally efficient hybrid chemistry schemes.

Data-driven modeling techniques for prediction of settled water turbidity in drinking water treatment

Drinking water treatment is a complex system of chemical, physical, and biological processes that is highly dependent on water quality and the design of the treatment process. To create decision-support tools, the prediction of key performance indicators, such as settled water turbidity, is needed. A variety of data-driven modeling techniques is available to formulate such predictions. Data-driven models provide valuable tools for formulating predictions where there is a lack of mechanistic models or the mechanisms are not fully understood, as in surface water treatment. The objective of this paper is to evaluate and compare the effectiveness of various data-driven techniques for this important, but difficult, problem. Recognizing that the size and quality of the dataset are most critical in this kind of analysis, this work uses one of the largest datasets used in this context consisting of 2,527 vectors of water quality and operational data (2,527 X nine data frame) from a full-scale water treatment plant. The paper constructs and compares the performance of the several data-driven models including k-nearest neighbor (KNN) regression, polynomial regression, and artificial neural networks (ANN). Based on test scaled root mean square error (RMSE), the ANN model was the most predictive (0.124). Similarly, the ANN model had the best predictive performance based on total scaled RMSE (0.086). These results show that ANNs have a high potential for the development of a future decision support system in selecting appropriate coagulant doses based on settled water turbidity.

Design of a novel switched fault estimator with adaption law for non-linear switched systems and application to chemical systems

Design of a novel switched fault estimator with adaption law for non-linear switched systems and application to chemical systems

Machine Learning Methods to Improve Crystallization through the Prediction of Solute–Solvent Interactions

Crystallization plays a crucial role in defining the quality and functionality of products across various industries, including pharmaceutical, food and beverage, and chemical manufacturing. The process’s efficiency and outcome are significantly influenced by solute–solvent interactions, which determine the crystalline product’s purity, size, and morphology. These attributes, in turn, impact the product’s efficacy, safety, and consumer acceptance. Traditional methods of optimizing crystallization conditions are often empirical, time-consuming, and less adaptable to complex chemical systems. This research addresses these challenges by leveraging machine learning techniques to predict and optimize solute–solvent interactions, thereby enhancing crystallization outcomes. This review provides a novel approach to understanding and controlling crystallization processes by integrating supervised, unsupervised, and reinforcement learning models. Machine learning not only improves product the quality and manufacturing efficiency but also contributes to more sustainable industrial practices by minimizing waste and energy consumption.

Characterization of antibiofilm compound from marine sponge Stylissa carteri.

The fouling phenomenon grabbed global attention and caused huge economic losses specifically in marine-related industries. Sessile behavior exposed the sponge to the risk of fouling. However, their bodies remained free from foulers, which were attributed to the chemical defense system. The objectives of this study were to determine the antibiofilm activity of the marine sponge, Stylissa carteri, and to characterize the isolated compound involved. The antibiofilm activity of S. carteri methanolic crude extract (MCE) and fractions was tested against biofilm-producing bacteria, Pseudomonas aeruginosa, using two different modes of crystal violet biofilm assays: preventive and detachment. Besides that, the disc-diffusion test was conducted to screen the antibacterial activity against gram-positive and gram-negative bacteria while a cytotoxicity assay was conducted on the HepG2 cell line. Bioassay-guided fractionation was carried out using vacuum liquid chromatography (VLC) and solid phase extraction using a C18 Sep-Pak Cartridge. The crystal compound was isolated and characterized through thin-layer chromatography (TLC), Fourier transform infrared (FTIR) spectroscopy, liquid chromatography-mass spectrometry (LCMS), and nuclear magnetic resonance (NMR) spectroscopy. The S. carteri MCE showed a promising result with a half-maximal inhibitory concentration (IC50) of 20.22μg/mL in the preventive assay, while no IC50 was determined in the detachment assay since all inhibitions < 50%. The S. carteri MCE exhibited broad-spectrum antibacterial activity and displayed a non-cytotoxic effect. Fraction 4 from MCE of S. carteri (IC50 = 2.40μg/mL) reduced the biofilm in the preventive assay at all concentrations and exhibited no antibacterial activity indicating the independence of antibiofilm from antibacterial properties. Based on the data obtained, an alkaloid named debromohymenialdisine (DBH) was identified from Fraction 4 of S. carteri MCE. In conclusion, S. carteri was able to reduce the establishment of the biofilm formed by P. aeruginosa and could serve as a prominent source of natural antifouling agents.

Periodic Stratification of Colloids in a Liquid Phase Produced by a Precipitation Reaction and Gel Swelling.

Pattern formation is a frequent phenomenon occurring in animate and inanimate systems. The interplay between the mass transport of the chemical species and the underlying chemical reaction networks generates most patterns in chemical systems. Periodic precipitation is an emblematic example of reaction-diffusion patterns, in which the process generates a spatial periodic structure in porous media. Here, we use the dormant reagent method to produce colloidal particles of Prussian blue (PB) and PB analogues at the liquid-gel interface. The generated particles produced a stable periodic stratification pattern in time in the liquid phase placed on top of the solid hydrogel. The phenomenon is governed by periodic swelling of the gel driven by the osmotic stress and stability of the formed particles. To illustrate the phenomenon, we developed an extended reaction-diffusion model, which incorporated the gel swelling and sedimentation effect of the formed colloids and could qualitatively reproduce the pattern formation in the liquid phase.

Regulating "Tip Effect" and Zn2+-Deposition Kinetics by In Situ Constructing Interphase for Low Voltage Hysteresis and Dendrite-Free Zn Anode.

Metal zinc (Zn) is being explored as a possible anode for aqueous zinc ion batteries (AZIBs). However, unrestrained Zn dendrite caused by "tip effect" and chemical corrosion continue to plague the Zn deposition process, limiting the functionality of AZIBs and prohibiting their use at high current densities. This work presents an in situ approach for introducing homogeneous ZnO nanoarrays onto the surface of Zn foil (Zn@ZnO NAs) as a functional protective interphase. On the one hand, well-distributed ZnO NAs protection layer can regulate the "tip effect" and confine the growth of Zn dendrite. On the other hand, the ZnO NAs layer can enhance the desolvation and diffusion process of Zn2+ on the surface of anode, attributing to low voltage hysteresis and exceptional electrochemical performance at high current densities. As a result, the Zn@ZnO NAs exhibits a low voltage hysteresis of 50.8mV with a superb lifespan of 1200h at a current density of 5mA cm-2. Moreover, Zn@ZnO NAs||α-MnO2 full-cell shows a superior cycling performance after 500 cycles at 0.5 A g-1 with a capacity of 216.69 mAh g-1. This work is expected to provide ideas for designing other reversible zinc anode chemical systems, especially under a high current density.

Thermodynamic and stoichiometric laws ruling the fates of growing systems

We delve into growing open chemical reaction systems (CRSs) characterized by autocatalytic reactions within a variable volume, which changes in response to these reactions. Understanding the thermodynamics of such systems is crucial for comprehending biological cells and constructing protocells because it sheds light on the physical conditions necessary for their self-replication. Building on our recent work, where we developed a thermodynamic theory for growing CRSs featuring basic autocatalytic motifs with regular stoichiometric matrices, we now expand this theory to include scenarios where the stoichiometric matrix has a nontrivial left kernel space. This extension introduces conservation laws, which limit the variations in chemical species due to reactions, thereby confining the system's possible states to those compatible with its initial conditions. By considering both thermodynamic and stoichiometric constraints, we clarify the environmental and initial conditions that dictate the CRSs' fate—whether they grow, shrink, or reach equilibrium. We also find that the conserved quantities significantly influence the equilibrium state achieved by a growing CRS. These results are derived independently of specific thermodynamic potentials or reaction kinetics, therefore underscoring the fundamental impact of conservation laws on the growth of the system. Published by the American Physical Society 2024

Benchmarking Ionization Potentials from pCCD Tailored Coupled Cluster Models.

The ionization potential (IP) is an important parameter providing essential insights into the reactivity of chemical systems. IPs are also crucial for designing, optimizing, and understanding the functionality of modern technological devices. We recently showed that limiting the CC ansatz to the seniority-zero sector proves insufficient in predicting reliable and accurate ionization potentials within an IP equation-of-motion coupled-cluster formalism. Specifically, the absence of dynamical correlation in the seniority-zero pair coupled cluster doubles (pCCD) model led to unacceptably significant errors of approximately 1.5 eV. In this work, we aim to explore the impact of dynamical correlation and the choice of the molecular orbital basis (canonical vs localized) in CC-type methods targeting 230 ionized states in 70 molecules, comprising small organic molecules, medium-sized organic acceptors, and nucleobases. We focus on pCCD-based approaches as well as the conventional IP-EOM-CCD and IP-EOM-CCSD. Their performance is compared to the CCSD(T) or CCSDT equivalent and experimental reference data. Our statistical analysis reveals that all investigated frozen-pair coupled cluster methods exhibit similar performance, with differences in errors typically within chemical accuracy (1 kcal/mol or 0.05 eV). Notably, the effect of the molecular orbital basis, such as canonical Hartree-Fock or natural pCCD-optimized orbitals, on the IPs is marginal if dynamical correlation is accounted for. Our study suggests that triple excitations are crucial in achieving chemical accuracy in IPs when modeling electron detachment processes with pCCD-based methods.

Protective effect of luteolin against oxidative stress‑mediated cell injury via enhancing antioxidant systems.

Physiological stress such as excessive reactive oxygen species (ROS) production may contribute normal fibroblasts activation into cancer‑associated fibroblasts, which serve a crucial role in certain types of cancer such as pancreatic, breast, liver and lung cancer. The present study aimed to examine the cytoprotective effects of luteolin (3',4',5,7‑tetrahydroxyflavone) against hydrogen peroxide (H2O2)‑generated oxidative stress in lung fibroblasts. To examine the effects of luteolin against H2O2‑induced damages, cell viability, sub‑G1 cell population, nuclear staining with Hoechst 33342, lipid peroxidation and comet assays were performed. To evaluate the effects of luteolin on the protein expression level of apoptosis, western blot assay was performed. To assess the antioxidant effects of luteolin, detection of ROS using H2DCFDA staining, O2‑ and ·OH using electron spin resonance spectrometer and antioxidant enzyme activity was performed. In a cell‑free chemical system, luteolin scavenges superoxide anion and hydroxyl radical generated by xanthine/xanthine oxidase and the Fenton reaction (FeSO4/H2O2). Furthermore, Chinese hamster lung fibroblasts (V79‑4) treated with H2O2 showed a significant increase in cellular ROS. Intracellular ROS levels and damage to cellular components such as lipids and DNA in H2O2‑treated cells were significantly decreased by luteolin pretreatment. Luteolin increased cell viability, which was impaired following H2O2 treatment and prevented H2O2‑mediated apoptosis. Luteolin suppressed active caspase‑9 and caspase‑3 levels while increasing Bcl‑2 expression and decreasing Bax protein levels. Additionally, luteolin restored levels of glutathione that was reduced in response to H2O2. Moreover, luteolin enhanced the activity and protein expressions of superoxide dismutase, catalase, glutathione peroxidase, and heme oxygenase‑1. Overall, these results indicated that luteolin inhibits H2O2‑mediated cellular damage by upregulating antioxidant enzymes.

Proper orthogonal descriptors for multi-element chemical systems

We introduce the proper orthogonal descriptors for efficient and accurate interatomic potentials of multi-element chemical systems. The potential energy surface of a multi-element system is represented as a many-body expansion of parametrized potentials which are functions of atom positions, atom types, and parameters. The proper orthogonal decomposition is employed to decompose the parametrized potentials as a linear combination of orthogonal basis functions. The orthogonal basis functions are used to construct proper orthogonal descriptors based on the elements of atoms, thus leading to multi-element descriptors. We compose the multi-element proper orthogonal descriptors to develop linear and quadratic interatomic potentials. We devise an algorithm to efficiently compute the total energy and forces of the interatomic potentials constructed from the proper orthogonal descriptors. The potentials are demonstrated for indium phosphide and titanium dioxide in comparison with the spectral neighbor analysis potential (SNAP) and atomic cluster expansion (ACE) potentials.

Managing Expectations and Imbalanced Training Data in Reactive Force Field Development: An Application to Water Adsorption on Alumina.

ReaxFF is a computationally efficient model for reactive molecular dynamics simulations that has been applied to a wide variety of chemical systems. When ReaxFF parameters are not yet available for a chemistry of interest, they must be (re)optimized, for which one defines a set of training data that the new ReaxFF parameters should reproduce. ReaxFF training sets typically contain diverse properties with different units, some of which are more abundant (by orders of magnitude) than others. To find the best parameters, one conventionally minimizes a weighted sum of squared errors over all of the data in the training set. One of the challenges in such numerical optimizations is to assign weights so that the optimized parameters represent a good compromise among all the requirements defined in the training set. This work introduces a new loss function, called Balanced Loss, and a workflow that replaces weight assignment with a more manageable procedure. The training data are divided into categories with corresponding "tolerances", i.e., acceptable root-mean-square errors for the categories, which define the expectations for the optimized ReaxFF parameters. Through the Log-Sum-Exp form of Balanced Loss, the parameter optimization is also a validation of one's expectations, providing meaningful feedback that can be used to reconfigure the tolerances if needed. The new methodology is demonstrated with a nontrivial parametrization of ReaxFF for water adsorption on alumina. This results in a new force field that reproduces both the rare and frequent properties of a validation set not used for training. We also demonstrate the robustness of the new force field with a molecular dynamics simulation of water desorption from a γ-Al2O3 slab model.

Acyl Phosphates as Chemically Fueled Building Blocks for Self‐Sustaining Protocells

Lipids spontaneously assemble into vesicle‐forming membranes. Such vesicles serve as compartments for even the simplest living systems. Vesicles have been extensively studied for constructing synthetic cells or as models for protocells—the cells hypothesized to have existed before life. These compartments exist almost always close to equilibrium. Life, however, exists out of equilibrium. In this work, we studied vesicle‐based compartments regulated by a non‐equilibrium chemical reaction network that converts activating agents. In this way, the compartments require a constant or periodic supply of activating agents to sustain themselves. Specifically, we use activating agents to condense carboxylates and phosphate esters into acyl phosphate‐based lipids that form vesicles. These vesicles can only be sustained when condensing agents are present; without them, they decay. We demonstrate that the chemical reaction network can operate on prebiotic activating agents, opening the door to prebiotically plausible, self‐sustainable protocells that compete for resources. In future work, such protocells should be endowed with a genotype, e.g., self‐replicating RNA structures, to alter the protocell's behavior. Such protocells could enable Darwinian evolution in a prebiotically plausible chemical system.

Acyl Phosphates as Chemically Fueled Building Blocks for Self-Sustaining Protocells.

Lipids spontaneously assemble into vesicle-forming membranes. Such vesicles serve as compartments for even the simplest living systems. Vesicles have been extensively studied for constructing synthetic cells or as models for protocells-the cells hypothesized to have existed before life. These compartments exist almost always close to equilibrium. Life, however, exists out of equilibrium. In this work, we studied vesicle-based compartments regulated by a non-equilibrium chemical reaction network that converts activating agents. In this way, the compartments require a constant or periodic supply of activating agents to sustain themselves. Specifically, we use activating agents to condense carboxylates and phosphate esters into acyl phosphate-based lipids that form vesicles. These vesicles can only be sustained when condensing agents are present; without them, they decay. We demonstrate that the chemical reaction network can operate on prebiotic activating agents, opening the door to prebiotically plausible, self-sustainable protocells that compete for resources. In future work, such protocells should be endowed with a genotype, e.g., self-replicating RNA structures, to alter the protocell's behavior. Such protocells could enable Darwinian evolution in a prebiotically plausible chemical system.

Predicting Serotonin Detection with DNA-Carbon Nanotube Sensors across Multiple Spectral Wavelengths.

Owing to the value of DNA-wrapped single-walled carbon nanotube (SWNT)-based sensors for chemically specific imaging in biology, we explore machine learning (ML) predictions DNA-SWNT serotonin sensor responsivity as a function of DNA sequence based on the whole SWNT fluorescence spectra. Our analysis reveals the crucial role of DNA sequence in the binding modes of DNA-SWNTs to serotonin, with a smaller influence of SWNT chirality. Regression ML models trained on existing data sets predict the change in the fluorescence emission in response to serotonin, ΔF/F, at over a hundred wavelengths for new DNA-SWNT conjugates, successfully identifying some high- and low-response DNA sequences. Despite successful predictions, we also show that the finite size of the training data set leads to limitations on prediction accuracy. Nevertheless, incorporating entire spectra into ML models enhances prediction robustness and facilitates the discovery of novel DNA-SWNT sensors. Our approaches show promise for identifying new chemical systems with specific sensing response characteristics, marking a valuable advancement in DNA-based system discovery.

Finding regulatory modules of chemical reaction systems

Within a cell, numerous chemical reactions form chemical reaction networks (CRNs), which are the origins of cellular functions. We previously developed a theoretical method called structural sensitivity analysis (SSA), which enables us to determine, solely from the network structure, the qualitative changes in the steady-state concentrations of chemicals resulting from the perturbations to a parameter. Notably, if a subnetwork satisfies specific topological conditions, it is referred to as a buffering structure, and the effects of perturbations to the parameter within the subnetwork are localized to the subnetwork (the law of localization). A buffering structure can be the origin of modularity in the regulation of cellular functions generated from CRNs. However, an efficient method to search for buffering structures in a large CRN has not yet been established. In this study, we prove the “inverse theorem” of the law of localization, which states that a certain subnetwork exhibiting a confined response range is always a buffering structure. In other words, we are able to identify buffering structures in terms of confined responses rather than the topological conditions. By leveraging this property, we develop an algorithm to enumerate all buffering structures for a given network by calculating responses. Additionally, we show that the hierarchy of perturbed response patterns corresponds to that of buffering structures, and present a method to illustrate the hierarchy. Our method will be a powerful tool for understanding the regulation of cellular functions generated from CRNs. Published by the American Physical Society 2024

Zincblende InAsxP1-x/InP Quantum Dot Nanowires for Telecom Wavelength Emission.

InAsxP1-x quantum dots (QDs) in InP nanowires (NWs) have been realized as a platform for emission at telecom wavelengths. These QDs are typically grown in NWs with the wurtzite crystal phase, but in this case, ultrathin diameters are required to achieve defect-free heterostructures, making the structures less robust. In this work, we demonstrate the growth of pure zincblende InAsxP1-x QDs in InP NWs, which enabled an increase in NW diameters to about 45 nm, achieved by employing Au-assisted vapor liquid solid growth in a chemical beam epitaxy system. We studied the growth of InP/InAsxP1-x heterostructures with different compositions to control the straight growth along the ⟨100⟩ direction and to tune the emission wavelength. Interestingly, we found that the growth mechanism for pure InAs QDs is different compared to that for InAsxP1-x alloy QDs. This allowed us to optimize different growth protocols to achieve straight growth of the final QD NWs. We successfully obtained the growth of InAsxP1-x QDs with a composition in the range of x = 0.24-1.00. By means of microphotoluminescence measurements, we demonstrate the tunability of the emission in dependence of the InAsxP1-x QD composition and morphology, remarkably observing an emission at the telecom O-band for a 10 nm thick QD with 80% of As content.

Customized Proteinaceous Nanoformulation for In Vivo Chemical Reprogramming.

Sweat gland (SwG) regeneration is crucial for the functional rehabilitation of burn patients. In vivo chemical reprogramming that harnessing the patient's own cells in damaged tissue is of substantial interest to regenerate organs endogenously by pharmacological manipulation, which could compensate for tissue loss in devastating diseases and injuries, for example, burns. However, achieving in vivo chemical reprogramming is challenging due to the low reprogramming efficiency and an unfavorable tissue environment. Herein, this work has developed a functionalized proteinaceous nanoformulation delivery system containing prefabricated epidermal growth factor structure for on-demand delivery of a cocktail of seven SwG reprogramming components to the dermal site. Such a chemical reprogramming system can efficiently induce the conversion of epidermal keratinocytesinto SwG myoepithelial cells, resulting in successful in situ regeneration of functional SwGs. Notably, in vivo chemical reprogramming of SwGs is achieved for the first time with an impressive efficiency of 30.6%, surpassing previously reported efficiencies. Overall, this proteinaceous nanoformulation provides a platform for coordinating the target delivery of multiple pharmacological agents and facilitating in vivo SwG reprogramming by chemicals. This advancement greatly improves the clinical accessibility of in vivo reprogramming and offers a non-surgical, non-viral, and cell-free strategy for in situ SwG regeneration.

On the development of steady-state and dynamic mass-constrained neural networks using noisy transient data

This paper presents the development of algorithms for mass-constrained neural network models that can exactly satisfy mass conservation laws of chemical process systems, even if the training data violates the same. As opposed to approximately satisfying mass balance constraints of a system by considering additional penalty terms in the objective function, algorithms have been developed to solve an equality-constrained optimization problem, thus ensuring the exact satisfaction of the overall mass conservation laws. For developing dynamic mass-constrained networks, hybrid series and parallel all-nonlinear static-dynamic neural network models are leveraged. The proposed algorithms for solving both the inverse and forward problems are tested by considering both steady-state and dynamic data in presence of varieties of noise characterizations. The proposed structures and algorithms are applied to the development of data-driven models of two nonlinear dynamic chemical processes, namely the Van de Vusse reactor system as well as a solvent-based post-combustion CO2 capture process.

The digital lab manager: Automating research support

Laboratory management automation is essential for achieving interoperability in the domain of experimental research and accelerating scientific discovery. The integration of resources and the sharing of knowledge across organisations enable scientific discoveries to be accelerated by increasing the productivity of laboratories, optimising funding efficiency, and addressing emerging global challenges. This paper presents a novel framework for digitalising and automating the administration of research laboratories through The World Avatar, an all-encompassing dynamic knowledge graph. This Digital Laboratory Framework serves as a flexible tool, enabling users to efficiently leverage data from diverse systems and formats without being confined to a specific software or protocol. Establishing dedicated ontologies and agents and combining them with technologies such as QR codes, RFID tags, and mobile apps, enabled us to develop modular applications that tackle some key challenges related to lab management. Here, we showcase an automated tracking and intervention system for explosive chemicals as well as an easy-to-use mobile application for asset management and information retrieval. Implementing these, we have achieved semantic linking of BIM and BMS data with laboratory inventory and chemical knowledge. Our approach can capture the crucial data points and reduce inventory processing time. All data provenance is recorded following the FAIR principles, ensuring its accessibility and interoperability.