Oxygenases as Powerful Weapons in the Microbial Degradation of Pesticides.

Oxygenases, which catalyze the reductive activation of O2 and incorporation of oxygen atoms into substrates, are widely distributed in aerobes. They function by switching the redox states of essential cofactors that include flavin, heme iron, Rieske non-heme iron, and Fe(II)/α-ketoglutarate. This review summarizes the catalytic features of flavin-dependent monooxygenases, heme iron-dependent cytochrome P450 monooxygenases, Rieske non-heme iron-dependent oxygenases, Fe(II)/α-ketoglutarate-dependent dioxygenases, and ring-cleavage dioxygenases, which are commonly involved in pesticide degradation. Heteroatom release (hydroxylation-coupled hetero group release), aromatic/heterocyclic ring hydroxylation to form ring-cleavage substrates, and ring cleavage are the main chemical fates of pesticides catalyzed by these oxygenases. The diversity of oxygenases, specificities for electron transport components, and potential applications of oxygenases are also discussed. This article summarizes our current understanding of the catalytic mechanisms of oxygenases and a framework for distinguishing the roles of oxygenases in pesticide degradation.

Interactions between nonfullerene acceptors lead to unstable ternary organic photovoltaic cells.

For organic photovoltaic (OPV) devices to achieve consistent performance and long operational lifetimes, organic semiconductors must be processed with precise control over their purity, composition, and structure. This is particularly important for high volume solar cell manufacturing where control of materials quality has a direct impact on yield and cost. Ternary-blend OPVs containing two acceptor-donor-acceptor (A-D-A)-type nonfullerene acceptors (NFAs) and a donor have proven to be an effective strategy to improve solar spectral coverage and reduce energy losses beyond that of binary-blend OPVs. Here, we show that the purity of such a ternary is compromised during blending to form a homogeneously mixed bulk heterojunction thin film. We find that the impurities originate from end-capping C=C/C=C exchange reactions of A-D-A-type NFAs, and that their presence influences both device reproducibility and long-term reliability. The end-capping exchange results in generation of up to four impurity constituents with strong dipolar character that interfere with the photoinduced charge transfer process, leading to reduced charge generation efficiency, morphological instabilities, and an increased vulnerability to photodegradation. As a consequence, the OPV efficiency falls to less than 65% of its initial value within 265 h when exposed to up to 10 suns intensity illumination. We propose potential molecular design strategies critical to enhancing the reproducibility as well as reliability of ternary OPVs by avoiding end-capping reactions.

Chemical Reaction of FA Cations Enables Efficient and Stable Perovskite Solar Cells.

Organometal halide perovskite solar cells (PSCs) have received great attention owing to a rapid increase in power conversion efficiency (PCE) over the last decade. However, the deficit of long-term stability is a major obstacle to the implementation of PSCs in commercialization. The defects in perovskite films are considered as one of the primary causes. To address this issue, isocyanic acid (HNCO) is introduced as an additive into the perovskite film, in which the added molecules form covalent bonds with FA cations via a chemical reaction. This chemical reaction gives rise to an efficient passivation on the perovskite film, resulting in an improved film quality, a suppressed non-radiation recombination, a facilitated carrier transport, and optimization of energy band levels. As a result, the HNCO-based PSCs achieve a high PCE of 24.41% with excellent storage stability both in an inert atmosphere and in air. Different from conventional passivation methods based on coordination effects, this work presents an alternative chemical reaction for defect passivation, which opens an avenue toward defect-mitigated PSCs showing enhanced performance and stability.

Computer-Aided Synthesis Planning (CASP) and Machine Learning: Optimizing Chemical Reaction Conditions.

Computer-aided synthesis planning (CASP) has garnered increasing attention in light of recent advancements in machine learning models. While the focus is on reverse synthesis or forward outcome prediction, optimizing reaction conditions remains a significant challenge. For datasets with multiple variables, the choice of descriptors and models is pivotal. This selection dictates the effective extraction of conditional features and the achievement of higher prediction accuracy. This review delineates the origins of data in conditional optimization, the criteria for descriptor selection, the response models, and the metrics for outcome evaluation, aiming to acquaint readers with the latest research trends and facilitate more informed research in this domain.

What Wilhelm Ostwald meant by "Autokatalyse" and its significance to origins-of-life research: Facilitating the search for chemical pathways underlying abiogenesis by reviving Ostwald's thought that reactants may also be autocatalysts: Facilitating the search for chemical pathways underlying abiogenesis by reviving Ostwald's thought that reactants may also be autocatalysts.

A closer look at Wilhelm Ostwald's articles that originally proposed the concept of autocatalysis reveals that he accepted reactants, not just products, as potential autocatalysts. Therefore, that a process is catalyzed by some of its products, which is the common definition of autocatalysis, is only a proper subset of what Ostwald meant by "Autokatalyse." As a result, it is necessary to reconsider the definition of autocatalysis, which is especially important for origins-of-life research because autocatalysis provides an abiotic mechanism that yields reproduction-like dynamics. Here, we translate and briefly review the two key publications on autocatalysis by Ostwald to revive his understanding of autocatalysis, and we introduce the concepts of recessive and expansive autocatalysis. Then we discuss the twofold significance of such a revival: first, facilitating the search for candidate processes underlying the origins of life, and second, updating our view of autocatalysis in complex reaction networks and metabolism.

Multistationarity questions in reduced versus extended biochemical networks.

We address several questions in reduced versus extended networks via the elimination or addition of intermediate complexes in the framework of chemical reaction networks with mass-action kinetics. We clarify and extend advances in the literature concerning multistationarity in this context, mainly from Feliu and Wiuf (J R Soc Interface 10:20130484, 2013), Sadeghimanesh and Feliu (Bull Math Biol 81:2428-2462, 2019), Pérez Millán and Dickenstein (SIAM J Appl Dyn Syst 17(2):1650-1682, 2018), Dickenstein et al. (Bull Math Biol 81:1527-1581, 2019). We establish general results about MESSI systems, which we use to compute the circuits of multistationarity for significant biochemical networks.

Graph-based, dynamics-preserving reduction of (bio)chemical systems.

Complex dynamical systems are often governed by equations containing many unknown parameters whose precise values may or may not be important for the system's dynamics. In particular, for chemical and biochemical systems, there may be some reactions or subsystems that are inessential to understanding the bifurcation structure and consequent behavior of a model, such as oscillations, multistationarity and patterning. Due to the size, complexity and parametric uncertainties of many (bio)chemical models, a dynamics-preserving reduction scheme that is able to isolate the necessary contributors to particular dynamical behaviors would be useful. In this contribution, we describe model reduction methods for mass-action (bio)chemical models based on the preservation of instability-generating subnetworks known as critical fragments. These methods focus on structural conditions for instabilities and so are parameter-independent. We apply these results to an existing model for the control of the synthesis of the NO-detoxifying enzyme Hmp in Escherichia coli that displays bistability.

Chemical gasification: An alternative approach to in vitro maturation of bovine oocytes.

This study aimed to evaluate the effect of chemical gasification and HEPES as alternative systems to pH control during in vitro maturation on bovine oocytes competence. Groups of 20 bovine cumulus oocytes complexes (COCs) were randomly distributed and cultured for 24 h in one of the following experimental groups: (i) chemical reaction (ChRG) system: CO2 generated from sodium bicarbonate and citric acid reaction (ii) culture media TCM-HEPES (HEPES-G); and (iii) control group (CNTG) in conventional incubator. After in vitro maturation (IVM), the COCs were in vitro fertilized (IVF), and in vitro cultivated (IVC) in a conventional incubator. We evaluated oocyte nuclear maturation, cleavage and blastocyst rates, in addition to the relative mRNA expression of BAX, BMP-15, AREG and EREG genes in oocytes and cumulus cells. The proportion of oocytes in metaphase II was higher in CNTG and ChRG (77.57% and 77.06%) than in the HEPES-G (65.32%; p = .0408 and .0492, respectively). The blastocyst production was similar between CNTG and ChRG (26.20% and 28.47%; p = .4232) and lower (p = .001) in the HEPES-G (18.71%). The relative mRNA expression of BAX gene in cumulus cells was significantly higher (p = .0190) in the HEPES-G compared to the CNTG. Additionally, the relative mRNA expression of BMP-15 gene was lower (p = .03) in oocytes from HEPES-G compared to the CNTG. In conclusion, inadequate atmosphere control has a detrimental effect on oocyte maturation. Yet, the use of chemical gasification can be an efficient alternative to bovine COCs cultivation.

Thioetherification of Br-Mercaptobiphenyl Molecules on Au(111).

Thioether polymers are fundamental for a variety of applications. Their synthesis is, however, more challenging than that of other metal-catalyzed reactions due to the reported detachment of the S atom during thermal activation. In this study, it has been demonstrated unambiguously that thermal annealing results in the thioetherification of the 4-bromo-4-mercaptobiphenyl molecule (Br-MBP) adsorbed on the surface of Au(111). Through complementary techniques, such as scanning tunneling microscopy, spectroscopy, and first-principle calculations, we have identified four reaction steps, involving sulfhydryl or bromine molecular functional groups and leading to the formation of intermolecular C-S bonds. To form the thioether polymer and to overcome the competitive formation of C-C bonds, two reaction steps, the dehalogenation, and dissociation of the S-Au bond, must occur simultaneously. We detail the electronic properties of the phenyl-sulfur bond and the polymer as a function of the ligand length. This result suggests a wider perspective of this chemical synthesis.

Changing the Interaction of a Single-Molecule Magnetic Moment with a Superconductor.

The exchange interaction of a brominated Co-porphyrin molecule with the Cooper pair condensate of Pb(111) is modified by reducing the Co-surface separation. The stepwise dehalogenation and dephenylation change the Co adsorption height by a few picometers. Only the residual Co-porphine core exhibits a Yu-Shiba-Rusinov bound state with low binding energy in the Bardeen-Cooper-Schrieffer energy gap. Accompanying density functional calculations reveal that the Co dz2 orbital carries the molecular magnetic moment and is responsible for the intragap state. The calculated spatial evolution of the Yu-Shiba-Rusinov wave function is compatible with the experimentally observed oscillatory attenuation of the electron-hole asymmetry with increasing lateral distance from the magnetic porphine center.

Generic algebraic conditions for the occurrence of switch-like behavior of a chemical kinetic system of the hypoxia pathway.

Weakly reversible chemical reaction networks with zero deficiency associated with mass-action kinetics admit, within each positive stoichiometric compatibility class, one positive steady state which is locally asymptotically stable and this irrespective of the values of the kinetics constants. Networks which do not enjoy these structural properties potentially exhibit more diverse dynamical behaviors. In this article, we consider a chemical reaction network associated with mass-action kinetics which is not weakly reversible and has a deficiency larger than one. The chemical reactions are at most bimolecular, but inflow and outflow reactions are present. Our results are as follows. We establish the existence of positive steady-state solutions and obtain their analytic expressions in the concentration space and in convex coordinates. We show that the system fulfills necessary conditions for a saddle-node and for a bifurcation into a saddle and a node. We apply a constructive approach to obtain a set of numerical values for the state variables and kinetic parameters, not reported previously, such that the reduced Jacobian is characterized by a zero eigenvalue with all other eigenvalues having negative real parts. The bifurcation diagram confirms the presence of the switch-like behavior.

Optimizing Neural Networks for Chemical Reaction Prediction: Insights from Methylene Blue Reduction Reactions.

This paper offers a thorough investigation of hyperparameter tuning for neural network architectures using datasets encompassing various combinations of Methylene Blue (MB) Reduction by Ascorbic Acid (AA) reactions with different solvents and concentrations. The aim is to predict coefficients of decay plots for MB absorbance, shedding light on the complex dynamics of chemical reactions. Our findings reveal that the optimal model, determined through our investigation, consists of five hidden layers, each with sixteen neurons and employing the Swish activation function. This model yields an NMSE of 0.05, 0.03, and 0.04 for predicting the coefficients A, B, and C, respectively, in the exponential decay equation A + B · e-x/C. These findings contribute to the realm of drug design based on machine learning, providing valuable insights into optimizing chemical reaction predictions.

Facile Solid-State Chemical Synthesis of CoMoO4 Nanorods for High-Performance Supercapacitors.

The development of electrode materials with excellent performance serves as the key for researchers to enhance the energy density of supercapacitors. Cobalt molybdate (CoMoO4) nanomaterials have been regarded as one of the most prospective electrode materials for supercapacitors due to their high theoretical capacitance and excellent electrical conductivity. In this paper, three kinds of CoMoO4 nanorods were prepared directly via simple and environmentally friendly solid-phase chemical reactions with solid inorganic salts as raw materials. According to X-ray powder diffraction (XRD) and scanning electron microscopy (SEM) test results, different reagents had certain effects on the size and morphology of CoMoO4, and these affected its electrochemical performance. In particular, the samples prepared with Co(NO3)2·6H2O as raw material took on a more uniform micromorphology, with a better crystallinity. Simultaneously, electrochemical test results showed that the samples synthesized with Co(NO3)2·6H2O presented relatively good electrical conductivity and a large specific capacitance (177 F g-1). This may be due to the nitrates reacting more slowly during the reaction and the crystals having difficulty aggregating during growth. Therefore, the structure of the prepared CoMoO4 nanomaterial was more uniform, and it was resistant to collapse during the charging and discharging process; thus, the capacitor presents the best performance.

Analysis of Entropy Generation via Non-Similar Numerical Approach for Magnetohydrodynamics Casson Fluid Flow with Joule Heating.

This analysis emphasizes the significance of radiation and chemical reaction effects on the boundary layer flow (BLF) of Casson liquid over a linearly elongating surface, as well as the properties of momentum, entropy production, species, and thermal dispersion. The mass diffusion coefficient and temperature-dependent models of thermal conductivity and species are used to provide thermal transportation. Nonlinear partial differential equations (NPDEs) that go against the conservation laws of mass, momentum, heat, and species transportation are the form arising problems take on. A set of coupled dimensionless partial differential equations (PDEs) are obtained from a set of convective differential equations by applying the proper non-similar transformations. Local non-similarity approaches provide an analytical approximation of the dimensionless non-similar system up to two degrees of truncations. The built-in Matlab (Version: 7.10.0.499 (R2010a)) solver bvp4c is used to perform numerical simulations of the local non-similar (LNS) truncations.

Chemical Reaction Modulated Low-Dimensional Phase Toward Highly Efficient Sky-Blue Perovskite Light-Emitting Diodes.

Blue perovskite light-emitting diodes (PeLEDs) are crucial avenues for achieving full-color displays and lighting based on perovskite materials. However, the relatively low external quantum efficiency (EQE) has hindered their progression towards commercial applications. Quasi-two-dimensional (quasi-2D) perovskites stand out as promising candidates for blue PeLEDs, with optimized control over low-dimensional phases contributing to enhanced radiative properties of excitons. Herein, the impact of organic molecular dopants on the crystallization of various n-phase structures in quasi-2D perovskite films. The results reveal that the highly reactive bis(4-(trifluoromethyl)phenyl)phosphine oxide (BTF-PPO) molecule could effectively restrain the formation of organic spacer cation-ordered layered perovskite phases through chemical reactions, simultaneously passivate those uncoordinated Pb2+ defects. Consequently, the prepared PeLEDs exhibited a maximum EQE of 16.6 % (@ 490 nm). The finding provides a new route to design dopant molecules for phase modulation in quasi-2D PeLEDs.

Fuel-Driven Dynamic Combinatorial Peptide Libraries.

Dynamic combinatorial chemistry (DCC) creates libraries of molecules that are constantly interchanging in a dynamic combinatorial library. When a library member self-assembles, it can displace the equilibria, leading to emergent phenomena like its selection or even its replication. However, such dynamic combinatorial libraries typically operate in or close to equilibrium. This work introduces a new dynamic combinatorial chemistry fueled by a catalytic reaction cycle that forms transient, out-of-equilibrium peptide-based macrocycles. The products in this library exist out of equilibrium at the expense of fuel and are thus regulated by kinetics and thermodynamics. By creating a chemically fueled dynamic combinatorial library with the vast structural space of amino acids, we explored the liquid-liquid phase separation behavior of the library members. The study advances DCCs by showing that peptide structures can be engineered to control the dynamic library's behavior. The work paves the way for creating novel, tunable material systems that exhibit emergent behavior reminiscent of biological systems. These findings have implications for the development of new materials and for understanding life's chemistry.

Photoinitiated Transient Self-Assembly in a Catalytically Driven Chemical Reaction Cycle.

Chemically fueled chemical reaction networks (CRNs) are essential in controlling dissipative self-assembly. A key challenge in the field is to store chemical fuel-precursors or "pre-fuels" in the system that are converted into activating or deactivating fuels in a catalytically controlled CRN. In addition, real-time control over catalysis in a CRN by light is highly desirable, but so far not yet achieved. Here we show a catalytically driven CRN that is photoinitiated with 450 nm light, producing activated monomers that go on to perform transient self-assembly. Monomer activation proceeds via photoredox catalysis, converting the monomer alcohol groups into the corresponding aldehydes that self-assemble into large supramolecular fibers. Monomer deactivation is achieved by organometallic catalysis that relies on pre-fuel hydrolysis to release formate (i.e. the deactivating fuel). Additionally, irradiation with 305 nm light accelerates the release of formate by photo-uncaging the pre-fuel, leading to a factor of ca. 2 faster deactivation of the monomer. Overall, we show transient self-assembly upon visible light photoactivation, and tunable life-times by ultraviolet light.

Beyond Single-Cycle Autonomous Molecular Machines: Light-Powered Shuttling in a Multi-Cycle Reaction Network.

Biomolecular machines autonomously convert energy into functions, driving systems away from thermodynamic equilibrium. This energy conversion is achieved by leveraging complex, kinetically asymmetric chemical reaction networks that are challenging to characterize precisely. In contrast, all known synthetic molecular systems in which kinetic asymmetry has been quantified are well described by simple single-cycle networks. Here, we report on a unique light-driven [2]rotaxane that enables the autonomous operation of a synthetic molecular machine with a multi-cycle chemical reaction network. Unlike all prior systems, the present one exploits a photoactive macrocycle, which features a different photoreactivity depending on the binding sites at which it resides. Furthermore, E to Z isomerization reverses the relative affinity of the macrocycle for two binding sites on the axle, resulting in a multi-cycle network. Building on the most recent theoretical advancements, this work quantifies kinetic asymmetry in a multi-cycle network for the first time. Our findings represent the simplest rotaxane capable of autonomous shuttling developed so far and offer a general strategy to generate and quantify kinetic asymmetry beyond single-cycle systems.

A Minimalistic Covalent Bond-Forming Chemical Reaction Cycle that Consumes Adenosine Diphosphate.

The development of synthetic active matter requires the ability to design materials capable of harnessing energy from a source to carry out work. Nature achieves this using chemical reaction cycles in which energy released from an exergonic chemical reaction is used to drive biochemical processes. Although many chemically fuelled synthetic reaction cycles that control transient responses, such as self-assembly, have been reported, the generally high complexity of the reported systems hampers a full understanding of how the available chemical energy is actually exploited by these systems. This lack of understanding is a limiting factor in the design of chemically fuelled active matter. Here, we report a minimalistic synthetic responsive reaction cycle in which adenosine diphosphate (ADP) triggers the formation of a catalyst for its own hydrolysis. This establishes an interdependence between the concentrations of the network components resulting in the transient formation of the catalyst. The network is sufficiently simple that all kinetic and thermodynamic parameters governing its behaviour can be characterised, allowing kinetic models to be built that simulate the progress of reactions within the network. While the current network does not enable the ADP-hydrolysis reaction to populate a non-equilibrium composition, these models provide insight into the way the network dissipates energy. Furthermore, essential design principles are revealed for constructing driven systems, in which the network composition is driven away from equilibrium through the consumption of chemical energy.

Kinetic Modeling and Parameter Estimation of a Prebiotic Peptide Reaction Network.

Although our understanding of how life emerged on Earth from simple organic precursors is speculative, early precursors likely included amino acids. The polymerization of amino acids into peptides and interactions between peptides are of interest because peptides and proteins participate in complex interaction networks in extant biology. However, peptide reaction networks can be challenging to study because of the potential for multiple species and systems-level interactions between species. We developed and employed a computational network model to describe reactions between amino acids to form di-, tri-, and tetra-peptides. Our experiments were initiated with two of the simplest amino acids, glycine and alanine, mediated by trimetaphosphate-activation and drying to promote peptide bond formation. The parameter estimates for bond formation and hydrolysis reactions in the system were found to be poorly constrained due to a network property known as sloppiness. In a sloppy model, the behavior mostly depends on only a subset of parameter combinations, but there is no straightforward way to determine which parameters should be included or excluded. Despite our inability to determine the exact values of specific kinetic parameters, we could make reasonably accurate predictions of model behavior. In short, our modeling has highlighted challenges and opportunities toward understanding the behaviors of complex prebiotic chemical experiments.