Chemical Systems Research Articles

Autonomous Motion of Hydrogels Driven by Semi-Interpenetrating Chemical Processing Systems.

Developing artificial autonomous materials is crucial for a deeper understanding of the emergence of life-like behavior. In nature, cells achieve autonomy through chemical processing systems incorporated into soft material-based frameworks. Inspired by natural cells, we herein describe a straightforward methodology for constructing artificial autonomous materials consisting of a polymer-based chemical processing system and a hydrogel-based soft framework. Using a material comprising a hydrogel framework devoid of active components in combination with semi-interpenetrating self-oscillating linear polymers, we discovered that semi-interpenetrating polymer-based chemical processing systems drive the autonomous motion of the hydrogel framework. The material exhibited autonomous volumetric oscillation powered by the Belousov-Zhabotinsky reaction. Furthermore, the autonomous behavior is controllable by changing the content of the chemical processing system incorporated into the hydrogel framework. Our findings shed light on a class of autonomous materials based on polymer-based chemical processing systems with soft frameworks.

Anion Recognition-Directed Supramolecular Catalysis with Functional Macrocycles and Molecular Cages.

ConspectusThe development of supramolecular chemistry has provided a variety of host molecules and noncovalent tools for boosting catalytic processes, stimulating the emergence and advance of supramolecular catalysis, among which macrocyclic and cage-like compounds have attracted great attention due to their possession of an enzyme-mimetic cavity and recognition ability. While the privileged scaffolds such as crown ethers, cyclodextrins, cucurbiturils, calixarenes, and metal-coordinated cages have been widely used, their skeletons usually do not contain a directional binding site; binding and activation mainly rely on cation-associated interactions or hydrophobic effects. In this context, the recent advance of anion supramolecular chemistry has drawn our attention to developing an anion recognition-directed approach by using tailor-made functionalized macrocycles and cages. Anions are important widely existing species in both biological and chemical systems and play an important role in regulating the structure and function of enzymes. We envisioned that by taking advantage of anions, including their rich variety, diverse geometry, and multiple interaction sites, the sophisticated cooperation of multiple noncovalent interactions can be manipulated in a confined cavity for directing efficient and selective catalysis.Following this concept, we initiated our study by introducing typical thiourea H-bonding groups to design and synthesize a series of bis-thiourea macrocycles, especially chiral macrocycles, by incorporating chiral linkers. Taking advantage of the obtained strong, cooperative anion binding, a macrocycle-enabled counteranion trapping strategy was developed, which afforded greatly enhanced catalytic efficiency and excellent stereocontrol in acid-catalyzing reactions. Furthermore, inspired by sulfate-induced macrocyclic dimerization assembly, we built a substrate-induced assembly system, enabling an induced-fit cooperative activation network for efficient and enantioselective catalysis. In addition, anion recognition-driven chirality gearing with a more sophisticated trithiourea cage was revealed, which could provide a basis for implementing anion-triggered allosteric catalysis within the induced helical space. Not limited to hydrogen bonding, the emerging anion-π interactions were largely exploited. A series of triazine-based prism cages containing three V-shaped electron-deficient π-cavities were constructed, and their anion-π binding properties were studied. Based on this system, cooperative anion-π activation was established for driving highly efficient and selective catalysis, which paved a way to push anion-π interactions toward more practical and useful catalyst design.These results demonstrated that the anion-recognition direction can serve as a powerful, versatile approach for boosting highly efficient and selective supramolecular catalysis. It is feasible not only for employing exogenous anions (e.g., counteranion) as a handle but also for recognition and regulation of anionic active intermediates/transition states, from use in conventional H-bonding to emerging anion-π recognition.

Effect of Different Chemical Pretreatments and Packaging Systems on Quality Characteristics of Fresh-Cut Pomegranate Arils (Punica granatum, L.)

Aims: Pomegranate (Punica granatum) is considered as challenging to consume due to its hard structure, hence used for fresh-cut production. Extending the shelf life of fresh-cut pomegranate arils would improve their marketability, transport efficiency, and reduce post-harvest losses. Study Design: Completely Randomized Design by using GRAPES (General R based Analysis Platform Empowered by Statistics). Place and Duration of Study: College of Agriculture, Kerala Agricultural University, February 2022-July 2024. Methodology: Pomegranate arils were extracted and treated with different pre-treatment solutions viz., 0.5% ascorbic acid, 0.5% citric acid, 1% calcium chloride and 1% calcium ascorbate for 4 minutes. The best pre-treatment solution was selected and arils were packaged in different packaging systems viz., shrink wrapping in 15µ polyolefin film, vacuum packaging in laminated pouches, MAP with N2 flushing in laminated pouches, aluminium tray wrapped with cling film and stored under two different storage conditions, after treating with 1% calcium ascorbate. Results: 1% calcium ascorbate was selected as the best pre-treatment solution and pre-treated arils kept in MAP with N2 flushing in laminated pouches stored under low temperature condition was found to be the best packaging and storage system. 1% calcium ascorbate treated arils in MAP with N2 flushing in laminated pouches stored under low temperature condition (10±5oC) had a shelf life of 6 days with superior physiological (PLW, percent leakage), chemical (acidity, vitamin C, phenol) and sensory characters (color, flavour, texture, appearance, taste and overall acceptability) as compared to control (2.00 days). Maximum vitamin C content of 23.02 mg 100g-1 and least phenol content of 214.16 mg 100g-1 were recorded on 6th day of storage. Conclusion: Pomegranate arils pre-treated with 1% calcium ascorbate for 4 minutes, kept in MAP with N2 flushing in laminated pouches stored under low temperature condition (10±5oC) can be considered as a protocol for shelf life extension of pomegranate arils.

Evaluating a quantum-classical quantum Monte Carlo algorithm with Matchgate shadows

Solving the electronic structure problem of molecules and solids to high accuracy is a major challenge in quantum chemistry and condensed matter physics. The rapid emergence and development of quantum computers offer a promising route to systematically tackle this problem. Recent work by [Huggins , ] proposed a hybrid quantum-classical quantum Monte Carlo (QC-QMC) algorithm using Clifford shadows to determine the ground state of a Fermionic Hamiltonian. This approach displayed inherent noise resilience and the potential for improved accuracy compared to its purely classical counterpart. Nevertheless, the use of Clifford shadows introduces an exponentially scaling postprocessing cost. In this work, we investigate an improved QC-QMC scheme utilizing the recently developed Matchgate shadows technique [], which removes the aforementioned exponential bottleneck. We observe from experiments on quantum hardware that the use of Matchgate shadows in QC-QMC is inherently noise robust. We show that this noise resilience has a more subtle origin than in the case of Clifford shadows. Nevertheless, we find that classical postprocessing, while asymptotically efficient, requires hours of runtime on thousands of classical CPUs for even the smallest chemical systems, presenting a major challenge to the scalability of the algorithm. Published by the American Physical Society 2024

Discovering dynamics and parameters of nonlinear oscillatory and chaotic systems from partial observations

Despite rapid progress in data acquisition techniques, many complex physical, chemical, and biological systems remain only partially observable, thus posing the challenge to identify valid theoretical models and estimate their parameters from an incomplete set of experimentally accessible time series. Here, we combine sensitivity methods and ranked-choice model selection to construct an automated hidden dynamics inference framework that can discover predictive nonlinear dynamical models for both observable and latent variables from noise-corrupted incomplete data in oscillatory and chaotic systems. After validating the framework for prototypical FitzHugh-Nagumo oscillations, we demonstrate its applicability to experimental data from squid neuron activity measurements and Belousov-Zhabotinsky reactions, as well as to the Lorenz system in the chaotic regime. Published by the American Physical Society 2024

Supramolecular Systems Chemistry Based on the Interplay between Peptides and Porphyrins

The functions of living organisms are emergent from networks of biomolecules. In this review, we discuss the creation of synthetic life‐like systems based on the interplay of peptides and porphyrins in the supramolecular chemical systems. In particular, we focus on the spatiotemporal control of self‐assembly processes, which allows for the development of hierarchical structures for biomimetic catalysis and adaptive dynamics for stimulus‐responsive structural transformations. Notably, when operating in a nonequilibrium regime—characterized by kinetic traps, feedback loops, and dissipative conditions—the structural landscape expands and system‐level properties emerge, including transient catalysis, metabolic self‐replication, and Darwinian‐like evolution. Controlling these systems at the biointerface would facilitate intelligent therapeutic interventions in the anti‐tumor phototherapy. Supramolecular systems chemistry provides a valuable framework for exploring new physicochemical spaces of peptides and porphyrins, and may offer distinct advantages and extensive applications across diverse fields.

Molecular misconceptions: A cross-national study on university students’ understanding of the mole concept

This study investigates university students’ understanding of the mole concept and its implications for chemistry education, highlighting the critical role of mathematical education. A questionnaire was administered to 303 students from universities in Panama, Mexico, Cuba, Chile, and Spain. The results reveal that only 29.7% of participants recognize the mole as a fundamental unit, while 20.8% confuse the amount of substance with a non-existent “Chemical System.” Only 18.5% correctly identified the substance quantity symbol as “n” and 32.7% were aware that Wilhelm Ostwald introduced the term mole, indicating deficiencies in historical knowledge. The significance of these findings highlights major misconceptions and gaps in both conceptual understanding and historical knowledge, underscoring the urgent need for revised teaching strategies. Addressing these issues is crucial for bridging the gap between theoretical knowledge and practical application, thereby enhancing instructional methods and optimizing chemistry education to improve students’ comprehension of fundamental concepts.

Design and Optimization of a Chemical Looping Hydrogen Production System Using Steam Reforming for Petrochemical Plants

Design and Optimization of a Chemical Looping Hydrogen Production System Using Steam Reforming for Petrochemical Plants

Weed management in organic dryland wheat production in the Pacific Northwest

AbstractGrowth in demand for organic small grains has increased interest in producing certified organic crops in the semiarid US Pacific Northwest. The region is well‐suited for small grain production, and there is a strong market for organic food products on the US West Coast. However, many growers encounter significant and persistent challenges with weed management, particularly management of perennial weeds such as Canada thistle [Cirsium arvense (L.) Scop.] and field bindweed (Convolvulus arvensis L.), but also common winter and spring annual grass weeds including cheatgrass (Bromus tectorum L.) and wild oat (Avena fatua L.). Coupled with the need to minimize soil disturbance, weed management can become nearly intractable and production limiting. From 2004 to 2024, several short and intermediate studies have been conducted to assess weed control tactics and crop rotation effects on weed management. Lessons learned include incorporating alfalfa (Medicago sativa L.) and spring barley (Hordeum vulgare L.) into rotations for suppression of field bindweed, or alfalfa and winter triticale (x Triticosecale Wittmack) for suppression of Canada thistle. Optimization of cultural inputs, particularly seeding rate, are critical for each crop in rotation. Animal integration and new crops such as quinoa (Chenopodium quinoa Willd.) are alternatives to conventional crops and potentially profitable. Incorporation of precision mechanical and chemical systems is feasible in narrow‐row cereals, and when combined with crop rotation, it could reduce or eliminate the need for repeated transitions back to convention production for organic growers.

Design principles, growth laws, and competition of minimal autocatalysts

The difficulty of designing simple autocatalysts that grow exponentially in the absence of enzymes, external drives or ingenious internal mechanisms severely constrains scenarios for the emergence of evolution by natural selection in chemical and physical systems. Here, we systematically analyze these difficulties in the simplest and most generic autocatalyst: a dimeric molecule that duplicates by templated ligation. We show that despite its simplicity, such an autocatalyst can achieve exponential growth autonomously. We also show, however, that it is possible to design as simple sub-exponential autocatalysts that have an advantage over exponential autocatalysts when competing for a common resource. We reach these conclusions by developing a theoretical framework based on kinetic barrier diagrams. Besides challenging commonly accepted assumptions in the field of the origin of life, our results provide a blueprint for the experimental realization of elementary autocatalysts exhibiting a form of natural selection, whether on a molecular or colloidal scale.

Olfactory and gustatory chemical sensor systems in the African turquoise killifish: Insights from morphology.

Smell and taste are extensively studied in fish species as essential for finding food and selecting mates while avoiding toxic substances and predators. Depending on the evolutionary position and adaptation, a discrete variation in the morphology of these sense organs has been reported in numerous teleost species. Here, for the first time, we approach the phenotypic characterization of the olfactory epithelium and taste buds in the African turquoise killifish (Nothobranchius furzeri), a model organism known for its short lifespan and use in ageing research. Our observations indicate that the olfactory epithelium of N. furzeri is organized as a simple patch, lacking the complex folding into a rosette, with an average size of approximately 600µm in length, 300µm in width, and 70µm in thickness. Three main cytotypes, including olfactory receptor neurons (CalbindinD28K), supporting cells (β-tubulin IV), and basal cells (Ki67), were identified across the epithelium. Further, we determined the taste buds' distribution and quantification between anterior (skin, lips, oral cavity) and posterior (gills, pharynx, oesophagus) systems. We identified the key cytotypes by using immunohistochemical markers, i.e. CalbindinD28K, doublecortin, and neuropeptide Y (NPY) for gustatory receptor cells, glial fibrillary acidic protein (GFAP) for supporting cells, and Ki67, a marker of cellular proliferation for basal cells. Altogether, these results indicate that N. furzeri is a microsmatic species with unique taste and olfactory features and possesses a well-developed posterior taste system compared to the anterior. This study provides fundamental insights into the chemosensory biology of N. furzeri, facilitating future investigations into nutrient-sensing mechanisms and their roles in development, survival, and ageing.

D’Arcy Thompson and Synthetic Biology—Then and Now

AbstractThough often presented as a recent scientific endeavor, synthetic biology began in the 19th century and was a particularly active field in the years preceding the publication of D’Arcy Thompson’s On Growth and Form. Much synthetic biology of the era was devoted to the construction of nonliving chemical systems that would undergo morphogenesis or dynamic behaviors which had been observed in living organisms. The point was to show that “life-like” structure and behavior could be generated by physicochemical laws and required no vitalist element. D’Arcy Thompson’s careful analysis of physicochemical morphogenetic mechanisms as possible explanations of organic form links closely to this way of thinking. In the modern era, when we can genetically engineer cells to undergo specific behaviors, and program cells to undergo simple morphogenetic behaviors of the kind that Thompson and others felt might underly natural morphogenesis, it is possible to test whether they will in fact produce a predictable multicellular shape. This addresses essentially the same questions about the morphogenetic role of physicochemical forces, such as surface tension, but does so “the other way round”: physicochemical mechanisms are not being used as models for morphogenesis by natural cells but rather as a means to engineer cells to make designed forms.

Research on Non-Equilibrium Dynamics Theory in Chemical Thermodynamics

Non-equilibrium dynamics theory is an important branch of chemical thermodynamics, widely applied in describing complex chemical reaction systems far from equilibrium. With the advancement of modern science and technology, an increasing number of chemical reactions exhibit unique behaviors under non-equilibrium conditions, such as self-organization and periodic oscillations—nonlinear phenomena. This paper systematically explores the distinctions between non-equilibrium and equilibrium states, introduces the fundamental principles of non-equilibrium thermodynamics, and analyzes cutting-edge issues such as nonlinear phenomena and multi-scale dynamics. Additionally, it discusses the specific applications of non-equilibrium dynamics in far-from-equilibrium reactions, heterogeneous catalysis, and industrial chemical processes, addressing both theoretical and experimental challenges. Through the combination of theoretical analysis and practical application, this paper aims to provide new insights and methodologies for research on non-equilibrium dynamics in the field of chemistry.

Reactivity of Atomic Oxygen Radical Anions in Metal Oxide Clusters.

Atomic oxygen radical anion (O⋅-) represents an important type of reactive centre that exists in both chemical and biological systems. Gas-phase atomic clusters can be studied under isolated and well controlled conditions. Studies of O⋅--containing clusters in the gas-phase provide a unique strategy to interpret the chemistry of O⋅- radicals at a strictly molecular level. This review summarizes the research progresses made since 2013 for the reactivity of O⋅- radicals in the atomically precise metal oxide clusters including negatively charged, nanosized, and neutral heteronuclear metal clusters benefitting from the development of advanced experimental techniques. New electronic and geometric factors to control the reactivity and product selectivity of O⋅- radicals under dark and photo-irradiation conditions have been revealed. The detailed mechanisms of O⋅- generation have been discussed for the reaction systems of nanosized and heteroatom-doped metal oxide clusters. The catalytic reactions mediated by the O⋅- radicals in metal clusters have also been successfully established and the microscopic mechanisms about the dynamic generation and depletion of O⋅- radicals have been clearly understood. The studies of O⋅- containing metal oxide clusters in the gas-phase provided new insights into the chemistry of reactive oxygen species in related condensed-phase systems.

Numerical study of a chemical looping combustion system: Effects of fuel type and particle size distributions on the operating performance

Chemical looping combustion (CLC) using biomass offers a promising pathway for achieving negative carbon emissions. However, the different properties of coal and biomass affect reaction performance, which has not been fully explored in the CLC system. Additionally, the effects of particle size distributions (PSDs) of solid fuels and oxygen carriers (OCs) are inadequately addressed. In this study, a 3D reactive model using the computational particle fluid dynamics (CPFD) method is developed to investigate the effects of PSDs of both solid fuels and oxygen carriers on operating performance with coal and biomass as fuels. Results indicate that the average residence time in FR of coal and biomass particles is 0.86 s and 0.68 s, respectively, but biomass outperforms coal in reaction efficiency. For coal-fueled CLC, the average residence time is 0.46 s for coal particles with diameters of 100–200 μm, whereas it reaches 0.86 s for coal diameters of 300–700 μm. However, the reaction performance is better with smaller coal sizes due to char gasification being the rate-limiting step. In comparison, for biomass-fueled CLC, the reaction efficiency remains consistent across various particle sizes under the specified conditions due to the rapid release of volatile components and the low content of char. Additionally, it is found that the OC conversion ratio is mainly determined by their residence time in the zones with relatively higher concentrations of the combustible gas in FR. Employing OCs with overly wide PSDs such as 300–800 μm can compromise the material seal above the AR, increase the risk of gas leakage and decrease the separation efficiency of the inertial separator.

Phase Prediction via Crystal Structure Similarity in the Periodic Number Representation.

The periodic number (PN) representation of the chemical systems, introduced by Dmitri Mendeleev, uncovers the fundamental principle of chemical similarity in a straightforward way. In this framework, the rows correspond to the principal quantum numbers of the elements' electronic configurations when considered isolated atoms. This systematic arrangement allows for a deeper understanding of the relationships and patterns among the elements. In this study, we propose a novel strategy for structure type (prototype) prediction by utilizing the PN concept to identify possible modifications and phase stability of unexplored chemical systems. Our PN-based crystal structure prediction (PNcsp) program, which evaluates similarity through PN neighboring in the phase map, provides the most probable prototypes for unknown and unreported modifications of given phases in binary and higher order chemical systems. We applied PNcsp to 59 distinct chemical systems whose equimolar phases are indicated in the respective phase diagrams but lack accurate experimental structure determination. Our methodology identified 93 prototypes for these 59 equiatomic phases, of which 47 exhibit mechanical and dynamic stability. Notably, this approach discovered 19 entirely novel, fully stable polymorphic phases, thereby expanding the known landscape of potential materials. Furthermore, we demonstrated that this method is also effective for nonequimolar and higher order systems.

Emergent Research Trends on the Structural Relaxation Dynamics of Molecular Clusters: From Structure-Property Relationship to New Function Prediction.

ConspectusMolecular clusters (MCs) are monodispersed, precisely defined ensembles of atom collections featured with shape-persistent architectures that can deliver certain functions independently. Their molecular compositions and surface functionalities can be tailored feasibly in a predefined manner, and they can be applied as basic structural units to be engineered into materials with desirable hierarchical structures and enriched functions. The chemical systems also offer great opportunities for the design and fabrication of soft structural materials without the chain topologies of polymers. The bulks of MC assemblies demonstrate viscoelasticity that is used to be considered as the unique feature of polymers, while the MC systems are distinct from polymers since their elasticities are resilient even at temperatures 100 K above their glass transition temperatures. The understanding of their anomalous viscoelasticity and the extended studies of general structure-property relationships are desired for the development of new chemical systems for emergent functions and the possibilities to resolve the intrinsic trade-offs of traditional materials.Meanwhile, general macroscopic functions or properties of materials are related to the transportation of mass, momentum, and/or energy, and they are basically realized or directed by the motions of structural units at different length scales. Structural relaxation dynamics research is critical in quantifying motions ranging from fast bond deformation, bond break/formation, and diffusion of ions and particles to the cooperative motions of structure units. Due to the advancement of measurement technology for relaxation dynamics (e.g., quasi-elastic scattering and broadband dielectric spectroscopy), the structural relaxation dynamics of MC materials have been probed for the first time, and their multiple relaxation modes across several temporal scales were systematically studied to bridge the correlation between molecular structures and macroscopic functions. The fingerprint information from dynamics studies, e.g., the temperature dependence of relaxation time and certain property, e.g., ion conductivity, was proposed to quantify the structure-property relationship, and the microscopic mechanism on the mechanical properties, ion conduction, and gas absorption and separation of MC materials can be fully understood.In this Account, to elucidate the uniqueness of MC materials, especially in comparison with polymers, four topics are mainly summarized: structural features, relaxation dynamics characterization techniques, relaxation dynamics characteristics, and quantified understanding of the structure-property relationship. The capability for new function prediction from relaxation dynamics studies is also introduced, and the typical example in impact resistant materials is provided. The Account aims to prove the significance of relaxation dynamics characterization for material innovation, while it also confirms the potential of MCs for functional material fabrications.

Contrasting Historical and Physical Perspectives in Asymmetric Catalysis: ΔΔG≠ versus Enantiomeric Excess

AbstractWith the rise of machine learning (ML), the modeling of chemical systems has reached a new era and has the potential to revolutionize how we understand and predict chemical reactions. Here, we probe the historic dependence on utilizing enantiomeric excess (ee) as a target variable and discuss the benefits of using relative Gibbs free activation energies (ΔΔG≠), grounded firmly in transition‐state theory, emphasizing practical benefits for chemists. This perspective is intended to discuss best practices that enhance modeling efforts especially for chemists with an experimental background in asymmetric catalysis that wish to explore modelling of their data. We outline the enhanced modeling performance using ΔΔG≠, escaping physical limitations, addressing temperature effects, managing non‐linear error propagation, adjusting for data distributions and how to deal with unphysical predictions,in order to streamline modeling for the practical chemist and provide simple guidelines to strong statistical tools. For this endeavor, we gathered ten datasets from the literature covering very different reaction types. We evaluated the datasets using fingerprint‐, descriptor‐, and graph neural network‐based models. Our results highlight the distinction in performance among varying model complexities with respect to the target representation, emphasizing practical benefits for chemists.

Contrasting Historical and Physical Perspectives in Asymmetric Catalysis: ▵▵G≠ versus Enantiomeric Excess.

With the rise of machine learning (ML), the modeling of chemical systems has reached a new era and has the potential to revolutionize how we understand and predict chemical reactions. Here, we probe the historic dependence on utilizing enantiomeric excess (ee) as a target variable and discuss the benefits of using relative Gibbs free activation energies (ΔΔG≠), grounded firmly in transition-state theory, emphasizing practical benefits for chemists. This perspective is intended to discuss best practices that enhance modeling efforts especially for chemists with an experimental background in asymmetric catalysis that wish to explore modelling of their data. We outline the enhanced modeling performance using ΔΔG≠, escaping physical limitations, addressing temperature effects, managing non-linear error propagation, adjusting for data distributions and how to deal with unphysical predictions,in order to streamline modeling for the practical chemist and provide simple guidelines to strong statistical tools. For this endeavor, we gathered ten datasets from the literature covering very different reaction types. We evaluated the datasets using fingerprint-, descriptor-, and graph neural network-based models. Our results highlight the distinction in performance among varying model complexities with respect to the target representation, emphasizing practical benefits for chemists.

A new chemical networked system: spatial-temporal evolution and control

Abstract This paper constructs a scale-free chemical network based on the Gierer-Meinhardt (GM) system and investigates its Turing instability. We establish a fractional-order single-node GM system with delay and design a fractional-order proportional derivative (PD) control strategy for the issue of bifurcation control. Using delay as bifurcation parameter, the existence of Hopf bifurcation is proven, and control over bifurcation threshold points is achieved through a fractional-order PD control strategy. For the scale-free chemical network based on the GM system, we obtain the condition of how the Turing instability occurs. We derive how the number of edges for the new nodes changes the stability of the network-organized system and investigate the relationship between degrees of nodes and eigenvalues of the network matrix. We give the instability condition caused by diffusion in the network-organized system. Finally, the numerical simulations verify analytical results.