Coccidiosis is a parasitic disease of a wide variety of animals caused by coccidian protozoa. The coccidia are responsible for major economic losses of the livestock industry. For example, the annual cost due to coccidiosis to the global poultry industry has been estimated to exceed US$ 3 billion annually. Currently available drugs for the control of this disease are either polyether ionophorous antibiotics that are derived from fermentation products, or synthetic compounds, produced by chemical synthesis. Unfortunately, no new drugs in either category have been approved for use for decades. Resistance has been documented for all those of the drugs currently employed and therefore the discovery of novel drugs with unique modes of action is imperative if chemotherapy is to remain the principal means to control this disease. This chapter aims to give an overview of the efficacy and mode of action of the current compounds used to control coccidiosis in livestock and provides a brief outlook of research needs for the future.
&NA; It is well known that a number of substances used in the textile industry can mean not only environmental, but also health problems. The scientific literature regarding potential adverse health effects of chemical substances in that industry is mainly related with human exposure during textile production. However, information about exposure of consumers is much more limited. Although most research on the health effects of chemicals in textiles concern allergic skin reactions, contact allergy is not the only potential human health problem. In this paper, we have reviewed the current scientific information regarding human exposure to chemicals through skin‐contact clothes. The review has been focused mainly on those chemicals whose probabilities of being detected in clothes were rather higher. Thus, we have revised the presence of flame retardants, trace elements, aromatic amines, quinoline, bisphenols, benzothiazoles/benzotriazoles, phthalates, formaldehyde, and also metal nanoparticles. Human dermal exposure to potentially toxic chemicals through skin‐contact textiles/clothes shows a non‐negligible presence in some textiles, which might lead to potential systemic risks. Under specific circumstances of exposure, the presence of some chemicals might mean non‐assumable cancer risks for the consumers.
Safety management in the chemical process industry remains a critical challenge due to recurring high impact industrial accidents and the limited predictive capability of conventional threshold based safety systems. Traditional PLC–SCADA frameworks rely on static alarm limits and reactive shutdown logic, which often fail to detect early stage nonlinear deviations in complex, multivariate processes. This study presents ChemSafeAI+, a machine learning driven dynamic safety and optimization framework designed to augment existing industrial control architectures. The system integrates real-time anomaly detection using gradient-boosting models, predictive analytics, safety action processing, operator aware visualization dashboards, and traceable console logging within a unified, modular architecture. The framework is evaluated using a validated synthetic dataset derived from the Haber–Bosch ammonia synthesis process, capturing realistic thermodynamic, kinetic, and operational variability across 5000 operating scenarios. Experimental results demonstrate strong anomaly detection capability and consistent early warning behavior across multiple abnormal operating conditions. SHAP-based explainability provides both global and local interpretability, aligning model decisions with domain relevant process variables. By combining predictive intelligence with safety oriented decision logic and operator traceability, ChemSafeAI+ demonstrates the feasibility of ML driven supervisory safety systems for proactive risk mitigation and improved operational resilience in industrial chemical environments.
Quantum coherence and other non-classical features are widely discussed in chemical dynamics, yet it remains difficult to quantify when such resources are operationally relevant for a given process and observable. While quantum resource theories provide a comprehensive framework for comparing free and resourceful settings, existing approaches typically rely on resource monotones or on performance bounds under free operations, and do not directly quantify the maximal influence a chosen resource can exert on a fixed chemical dynamics. Here, we introduce task specific, process level quantifiers that upper bound the largest change a quantum resource can induce in a target figure of merit. Central is a resource impact functional $\mathcal{C}_M(Λ)$, defined by comparing a state with its paired resource-free counterpart under the same quantum channel $Λ$, which admits an operational interpretation in binary hypothesis testing. We derive variation and time bounds that constrain how rapidly a resource can modify a target signal, providing resource-aware analogues of quantum speed limits. Moreover, we show that open system dynamics can be decomposed into free and resourceful components such that only the resourceful component contributes to $\mathcal{C}_M(Λ)$, thereby isolating the parts of a generator responsible for resource-induced changes in the observable. We illustrate the framework exemplary for energy transfer in a donor-acceptor dimer in two analytically solvable regimes. Our results provide a general toolbox for diagnosing and benchmarking quantum resource effects in molecular processes.
The K + KRb $\longrightarrow$ Rb + K$_{2}$ chemical reaction is the first ultracold atom-diatom chemical reaction for which experimental results have been reported for temperatures below 1 $μ$K more than a decade ago. The reaction occurs through coupling with an excited electronic state that is accessible even in the ultracold limit. A previous quantum dynamics study, excluding non-adiabatic effects, has reported a rate coefficient that is about 35\% below the experimental value. Here, we report the first non-adiabatic quantum dynamics study of this reaction and obtain rate coefficients in better agreement with experiments. Our results show that short-range dynamics mediated by coupling with the excited electronic state introduces quantum interference effects that influence both the state-to-state rate coefficients and the overall reaction rates.
Entanglement is a crucial resource for achieving quantum advantages in quantum computation, quantum sensing, and quantum communication. As shown in this Letter, entanglement is also a valuable resource for the coherent control of the large class of bimolecular chemical reactions. We introduce an entanglement-enhanced coherent control scheme, in which the initial preparation of the superposition state is divided into two steps: the first entangles the reactants, and the second is responsible for coherent control. This approach can overcome the limitations of traditional coherent control of scattering caused by non-interfering pathways, known as satellite terms. By tuning the amount of entanglement between reactants, the visibility of coherent control in chemical reactions can be modulated and optimized. Significantly, there exists an optimal amount of entanglement, which ensures complete indistinguishability of the reaction pathways, maximizing the extent of control. This entanglement-enhanced coherent control scheme is computationally illustrated using the ultracold KRb + KRb reaction, where a perfect control over the parity of the product rotational states is achieved.
The different temperatures associated with the climatic conditions of each continent and each biome directly influence the exposure properties of each material used in each region, including hydraulic cement, an important material widely employed in bridges, viaducts, and buildings worldwide. Despite being prepared at elevated temperatures, hydraulic cement is often stored and used under ambient conditions, posing challenges, particularly in tropical environments. The present work investigates the effects of different temperatures (10 °C, 30 °C, and 50 °C) on the deterioration of hydraulic cement and microstructural and mechanical behaviors. Kinect investigations were carried out to advance a chemical formalism of the deterioration of cement stored at different temperatures in a tropical climate. Signs of chemical deterioration of cement samples were investigated by XRD and SEM analyses, which revealed the presence of essential phases on the surface of the mortars, such as Portlandite, CSH, and Ettringite. The study incorporated gray residue into the mortar mixtures in two forms: addition (type B mortar) and substitution (type C mortar). For type B, 10 % of gray residue was added as an additive without reducing the cement content, while for type C, 10 % of the cement was replaced with gray residue to lower environmental impact. The presence of gray residue contributed to the hydration kinetics and microstructure, enhancing the formation of CSH phases, which are critical for mechanical strength. Mechanical performance revealed that type A (reference mortar) suffered a 6 % reduction in compressive strength after 90 days of storage at ambient conditions, while type B showed a 23 % increase due to the addition of ash residue, and type C, although with a 33 % reduction, balanced lower cement use with environmental benefits and mitigated losses related to chemical deterioration. Finally, sustainable mortars showed better mechanical performance than traditional ones, especially when the cement was stored at 50 °C, as predicted by the kinetic formalism (R² = 0.99 across storage conditions).
SrFeO3-based oxides have emerged as promising cathodes for proton-conducting solid oxide fuel cells (H-SOFCs), yet their performance at intermediate temperatures remains unsatisfactory. To overcome this limitation, we developed a multielement doping strategy, resulting in the synthesis of a novel oxide, SrFe0.9Nb0.025Ta0.025Mo0.025W0.025O3 (ME-SFO). Unlike conventional SrFeO3 materials doped with single elements, ME-SFO has a remarkable synergistic effect that substantially enhances both proton and oxygen transport kinetics. Compared with the SrFe0.9X0.1O3 cathode, the ME-SFO cathode has superior reaction kinetics, achieving the lowest polarization resistance and activation energy. This enhanced catalytic activity translates into outstanding performance in H-SOFC applications, delivering a peak power density of 1748 mW∙cm−2 at 700 °C—surpassing not only single-doped SrFe0.9X0.1O3 variants but also other SrFeO3-based cathodes reported for H-SOFCs. Although good long-term stability is achieved at a fixed temperature, ME-SFO suffers from high thermal expansion, which compromises cycling stability, leading to noticeable current density degradation after 10 test cycles. To mitigate this issue, we incorporated the negative thermal expansion oxide NdMnO3 into the cathode, effectively counterbalancing the thermal expansion of ME-SFO. The optimized composition, ME-SFO (80 wt%) + NdMnO3 (20 wt%), significantly improved cycling stability while maintaining high performance. This modification enhances the cathode/electrolyte interfacial condition, further increasing the fuel cell output to 1888 mW∙cm−2 at 700 °C. Coupling NdMnO3 with ME-SFO represents a “one stone, two birds” strategy, simultaneously improving both the power output and cycling stability. This advancement positions ME-SFO as a highly competitive cathode material for H-SOFCs, offering a balanced combination of electrochemical performance, durability, and cycling stability.
Next-generation processing and computing technologies (NGPTs), being new platforms that provide high energy efficiency and process intensification, are touted as key decarbonisation enablers in the ceramic and glass manufacturing sectors, but there are adoption barriers to overcome. Through a critical literature review and thematic analysis of ceramics and glass stakeholders’ interviews and workshop data, twelve consolidated barriers were identified, broadly grouped into economic, organisational, external & regulatory, and operational & technological factors. The Interpretive Structural Modelling (ISM) technique was adopted to deepen the understanding of the contextual interactions and interdependencies among the barriers, structuring them into seven hierarchical layers. This ISM output was further complemented with a MICMAC analysis to determine the dependence and driving powers of the barriers. Regulatory ambiguity alongside a lack of digital strategy were established to be the two most influential barriers, with knock-on effects on the remaining ones, in the interplay between NGPTs adoption and digital transformation towards net-zero, in both sectors. The results offer uniquely useful policy insights and practical managerial levers for ceramics and glass stakeholders regarding targeted intervention options prioritisation when designing response strategies for overcoming the barriers.
Optimization of chemical systems and processes have been enhanced and enabled by the guidance of algorithms and analytical approaches. While many methods will systematically investigate how underlying variables govern a given outcome, there is often a substantial number of experiments needed to accurately model these relations. As chemical systems increase in complexity, inexhaustive processes must propose experiments that efficiently optimize the underlying objective, while ideally avoiding convergence on unsatisfactory local minima. We have developed the Paddy software package around the Paddy Field Algorithm, a biologically inspired evolutionary optimization algorithm that propagates parameters without direct inference of the underlying objective function. Benchmarked against the Tree of Parzen Estimator, a Bayesian algorithm implemented in the Hyperopt software Library, Paddy displays efficient optimization with lower runtime, and avoidance of early convergence. Herein we report these findings for the cases of: global optimization of a two-dimensional bimodal distribution, interpolation of an irregular sinusoidal function, hyperparameter optimization of an artificial neural network tasked with classification of solvent for reaction components, and targeted molecule generation via optimization of input vectors for a decoder network. We anticipate that the facile nature of Paddy will serve to aid in automated experimentation, where minimization of investigative trials and or diversity of suitable solutions is of high priority.
Quantum chemical calculations (QCC) are computational techniques to analyze the characteristics of molecules. The variational quantum eigensolver (VQE) designed for noisy intermediate-scale quantum (NISQ) computers can be used to calculate the ground-state energies of molecules, while coupled-cluster with singles, doubles, and perturbative triples [CCSD(T)] is regarded as a traditional gold standard algorithm in QCC. The advantage between CCSD(T) and VQE in terms of the accuracy of ground-state energy calculation differs depending on molecular structures. In this work, we propose an automatic algorithm switching (AAS) technique to accurately calculate the ground-state energies of a target molecule with different bond distances. It automatically switches CCSD(T) and VQE by identifying a bond distance where the accuracy of CCSD(T) begins to drop for a target molecule. Our evaluation using a noise-less quantum computer simulator demonstrates that AAS improves the accuracy to describe the bond breaking processes of molecules compared to CCSD(T) and VQE.
ABSTRACT A series of low‐carbon fly ash cenosphere plates was prepared using solid waste based cementitious material as binder through a simple process; meanwhile, effects of cenosphere's particle size and size distribution on microstructure and properties were investigated. In the range of 20–200 mesh, as particle size decreases, sound absorption performance decreases while strength improves. Appropriate particle grading can effectively improve the compressive strength, but it will lead to a significant decrease in sound absorption performance. Samples prepared from fly ash cenosphere with a particle size of 20–40 mesh show good sound absorption performance: when the cavity size is 0 mm, the maximum sound absorption coefficient is 0.64 and the average value is 0.36 in the range of 50–1 600 Hz; when the cavity size is 100 mm, sound absorption coefficient at 100 Hz is 0.47, the maximum sound absorption coefficient in the range of 50–1 600 Hz is 0.90, and the average value is 0.57 in the range of 100–500 Hz. The prepared low‐carbon fly ash cenosphere plates show excellent sound absorption performance at low‐frequency and are expected to have broad application prospects in low‐frequency noise reduction and absorption.
Modern potential energy surfaces have shifted attention to molecular simulations of chemical reactions. While various methods can estimate rate constants for conformational transitions in molecular dynamics simulations, their applicability to studying chemical reactions remains uncertain due to the high and sharp energy barriers and complex reaction coordinates involved. This study focuses on the thermal cis-trans isomerization in retinal, employing molecular simulations and comparing rate constant estimates based on one-dimensional rate theories with those based on sampling transitions and grid-based models for low-dimensional collective variable spaces. Even though each individual method to estimate the rate passes its quality tests, the rate constant estimates exhibit disparities of up to four orders of magnitude. Rate constant estimates based on one-dimensional reaction coordinates prove challenging to converge, even if the reaction coordinate is optimized. However, consistent estimates of the rate constant are achieved by sampling transitions and by multi-dimensional grid-based models.
Hydrazine stands out as the most generally used chemical-reducing agent for reducing graphene oxide. Despite numerous experimental and theoretical investigations into the reduction reaction, the reduction mechanism remains unclear. In this study, we propose that, in aqueous hydrazine solutions, both hydrazine and hydroxide ions could initiate the reduction of graphene oxide. We introduce a chemical reaction pathway involving C-H cleavage and a dehydroxylation process for the reduction of graphene oxide. By utilizing density functional theory calculations, the reduction reactions mediated by hydrazine and hydroxide ions are separately investigated. The reaction routes on the basal plane and edge regions of graphene oxide are discussed independently. The density functional theory calculations demonstrate that the proposed mechanism is both thermodynamically and dynamically feasible. This work might contribute to an atomic-level comprehension of a longstanding challenge in the field of graphene oxide.
Thiolate containing mercury(II) complexes of the general formula [Hg(SR)$_n$]$^{2-n}$ have been of great interest since the toxicity of mercury was recognized. $^{199}$Hg nuclear magnetic resonance spectroscopy (NMR) is a powerful tool for characterization of mercury complexes. In this work, the Hg shielding constants in a series of [Hg(SR)$_n$]$^{2-n}$ complexes are therefore investigated computationally with particular emphasis on their geometry dependence. Geometry optimizations and NMR chemical shift calculations are performed at the density functional theory (DFT) level with both the zeroth-order regular approximation (ZORA) and four-component relativistic methods. The four exchange-correlation (XC) functionals PBE0, PBE, B3LYP and BLYP are used in combination with either Dyall's Gaussian-type (GTO) or Slater-type orbitals (STOs) basis sets. Comparing ZORA and four-component calculations, one observes that the calculated shielding constants for a given molecular geometry have a constant difference of $\sim$1070 ppm. This confirms that ZORA is an acceptable relativistic method to compute NMR chemical shifts. The combinations of 4-component/PBE0/v3z and ZORA/PBE0/QZ4P are applied to explore the geometry dependence of the isotropic shielding. For a given coordination number the distance between mercury and sulfur is the key factor affecting the shielding constant, while changes in bond and dihedral angles and even different side groups have relatively little impact.