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The study investigated the impact of virtual laboratory on the achievements of secondary school chemistry students in homogeneous and heterogeneous collaborative settings in Minna, Niger state, Nigeria. Three hypotheses were formulated, analyzed and tested at 0.05 alpha level. Stratified sampling technique was used to select 60 Senior Secondary Class Two (SS II) Chemistry Students. The subjects were stratified along gender and ability levels. Sixty students were randomly selected from two secondary schools in Minna, Nigeria. Thirty students (male, n = 15; female, n = 15) were selected from each school. Pretest, posttest, and experimental group design was employed. Experts validated 20-item multiple-choice Chemistry Achievement Test (CAT) was used for data collection. A reliability coefficient of 0.91 was obtained from the pilot testing using Kuder Richardson (KR-20). ANCOVA and Sidak post-hoc statistics were used for testing the hypotheses at 0.05 level of significance. The results showed that: (a) students in homogeneous ability grouping taught Chemistry using virtual laboratory instruction in collaborative setting performed better than their counterpart taught Chemistry in heterogeneous grouping composition; (b) female students in homogeneous group performed better than their counterparts in heterogeneous groups; (c) the higher achiever students in homogeneous groups outperformed those in heterogeneous groups. Based on these findings, it was recommended that the use of virtual laboratory instruction in homogeneous gender and ability level grouping in collaborative setting should be encouraged in teaching practical chemistry at senior secondary schools in Nigeria.

Machine Learning (ML) stands as a disruptive technology, finding application across a diverse array of scientific disciplines. When applied to homogeneous catalysis, this technology accelerates catalyst discovery through virtual screening, which not only reduces experimental iterations but also yields significant savings in time, resources, and waste generation. ML algorithms, often integrated with cheminformatic tools and quantum mechanics featurization, excel in predicting reaction outcomes that guide the engineering of catalysts for desired reactivity and selectivity. This minireview presents recent studies regarding databases as well as supervised and unsupervised problems, offering a general yet insightful perspective on the current ML-driven progress in homogeneous catalysis.

Photocatalytic reactions, achieving chemical synthesis in a more sustainable manner than thermal reactions, have been demonstrated to become more efficient, greener and easier to scale up when combined with continuous microflow technology. Nevertheless, the report on the kinetics measurement for photocatalytic reactions in continuous microflow, especially in a fully automated way, is very rare. In this work, two challenging parameters, i.e., the reaction order with respect to oxygen (2.48) and photoreaction activation energy (-16.83 kJ/mol) of the photocatalytic oxidation of 9,10-diphenylanthracene, were acquired in an automated continuous flow platform using the Steady-state Method. Moreover, the Ramping Method was also successfully implemented in the automated continuous flow photoreaction platform, exhibiting a predictive accuracy of 4.42 %, with 64.3 % less time and 58.0 % less material consumption than the Steady-state Method. And it was found that the improvement in the residence time distribution of the microreactor could improve the accuracy of the Ramping Method. The automated continuous flow process developed in this work could offer an efficient and accurate way to attain the reaction kinetics information for homogeneous photocatalytic reactions.

A heterogeneous base catalyst transesterification process with a calcium oxide (CaO) catalyst was performed to produce high-purity methyl ester (ME) from pretreated sludge palm oil (PSPO) derived from sludge palm oil (SPO). Additionally, a comparative analysis was conducted with potassium hydroxide (KOH) as a homogeneous base catalyst to assess the distinctions between heterogeneous and homogeneous base catalysts. The response surface methodology (RSM) was utilized to determine the optimal and recommended conditions for both transesterification processes. For heterogeneous transesterification, a varying CaO catalyst loading (10–60 wt.%), methanol (25–65 wt.%), and reaction time (60–180 min) were essential parameters. Meanwhile, homogeneous transesterification involved investigating the KOH catalyst loading (1–3 wt.%), methanol (1.8–5.5 wt.%), and reaction time (20–60 min). For the heterogeneous-base-catalyzed reaction, the recommended conditions were as follows: a molar ratio of methanol to oil of 5.83:1 (41.61 wt.%), 31.3 wt.% CaO, and a reaction time of 119.0 min, which resulted in a ME purity of 96.51 wt.%. The optimal conditions for homogeneous transesterification were a molar ratio of methanol to oil of 0.49:1 (3.45 wt.%), a 40 min reaction time, and a 1.39 wt.% KOH concentration, which achieved 96.59 wt.% ME and met the standard.

Purpose: The purpose of this study was to develop the augmented reality (AR) educational program combined with the instructional guidance for supportive learning, which enhanced the thinking process cooperative discussion and problem-solving skills in chemistry subject. Design/methodology/approach: The method used the quasi-experimental research design. Of the 45 students who attended this experiment, only 25 with low achievement qualified in operating the AR learning system of saponification and transesterification environment (ARLS-STE) system. Findings: These results confirmed that the AR educational program could have increased substantial benefits in improvements of students' knowledge and the ability of the thinking process for the participants with the lowest score. In semi-structured interviews, most of participants enjoyed manipulating the ARLS-STE system, which was realistic, motived and interesting for learning science subjects. Originality/value: The low-achieving students have often been known with a low learning capability, and they lack in developing constructional knowledge, despite being keen for learning. Regarding educational concerns for this population, providing orientated learning and supportive materials could increase their learning effects. Virtual worlds are an efficient learning tool in educational setting. The AR can offer visual concepts and physical interaction for students with low achievement in learning. Thus, this study investigates the acceptability of an educational program designed in the ARLS-STE, which involves the learning effects of academic knowledge and the capability of thinking process for students with low achievement. The ARLS-STE system was developed for this proposal, based upon the marker-based AR technologies combined with hands-on manipulation.

Abstract Identification of interactions between chemical compounds and proteins is crucial for various applications, including drug discovery, target identification, network pharmacology, and elucidation of protein functions. Deep neural network-based approaches are becoming increasingly popular in efficiently identifying compound-protein interactions with high-throughput capabilities, narrowing down the scope of candidates for traditional labor-intensive, time-consuming and expensive experimental techniques. In this study, we proposed an end-to-end approach termed SPVec-SGCN-CPI, which utilized simplified graph convolutional network (SGCN) model with low-dimensional and continuous features generated from our previously developed model SPVec and graph topology information to predict compound-protein interactions. The SGCN technique, dividing the local neighborhood aggregation and nonlinearity layer-wise propagation steps, effectively aggregates K-order neighbor information while avoiding neighbor explosion and expediting training. The performance of the SPVec-SGCN-CPI method was assessed across three datasets and compared against four machine learning- and deep learning-based methods, as well as six state-of-the-art methods. Experimental results revealed that SPVec-SGCN-CPI outperformed all these competing methods, particularly excelling in unbalanced data scenarios. By propagating node features and topological information to the feature space, SPVec-SGCN-CPI effectively incorporates interactions between compounds and proteins, enabling the fusion of heterogeneity. Furthermore, our method scored all unlabeled data in ChEMBL, confirming the top five ranked compound-protein interactions through molecular docking and existing evidence. These findings suggest that our model can reliably uncover compound-protein interactions within unlabeled compound-protein pairs, carrying substantial implications for drug re-profiling and discovery. In summary, SPVec-SGCN demonstrates its efficacy in accurately predicting compound-protein interactions, showcasing potential to enhance target identification and streamline drug discovery processes. Scientific contributions The methodology presented in this work not only enables the comparatively accurate prediction of compound-protein interactions but also, for the first time, take sample imbalance which is very common in real world and computation efficiency into consideration simultaneously, accelerating the target identification and drug discovery process.

Roads are the most heavily affected aspect of urban infrastructure given the ever-increasing number of vehicles needed to provide mobility to residents, supply them with goods, and help sustain urban growth. An important indicator of degrading road infrastructure is the so-called bump features of the road surface (BFRS), which have affected transportation safety and driving experience. To collect BFRS, we can collect discrete-sampled, non-homogeneous multi-sensor stream data. We propose a BFRS detection method based on spectrum modeling and multi-dimensional features. With the sampling rate of GPS at 1 Hz and a gyroscope and accelerometer at 100 Hz, multi-sensor stream data are recorded at three different urban areas of Nanjing, China, using the smartphone mounted on a vehicle. The recorded stream data captures a geometric feature modeling movement and the respective driving conditions. Derived features also include acceleration, orientation, and speed information. To capture bump features, we develop a deep-learning-based approach based on so-called spectrum features. BFRS detection experiments using multi-sensor stream data from smartphones are conducted, and 4, 14, and 17 BFRS are correctly detected in three different areas, with the precision as 100%, 70.00%, and 77.27%, respectively. Then, comparisons are conducted between the proposed method and three other methods, and the F-score of the proposed method is computed as 1.0000, 0.6363, and 0.7555 at three different areas, which hold the highest value among all results. Finally, it shows that the proposed method performs well in different geographic areas.

The physical capabilities of a reconfigurable assembly system (RAS) increase the agility and responsiveness of the system in highly volatile market conditions. However, achieving optimal RAS utilization entails solving complex optimization problems effectively and efficiently. These optimizations often define homogenous sets of problem instances. While algorithm configuration in such homogeneous contexts traditionally adopts a “one-size-fits-all” approach, recent studies have shown the potential of per-instance algorithm configuration (PIAC) methods in these settings. In this work, we evaluate and compare the performance of different PIAC methods in this context, namely Hydra—a state-of-the-art PIAC method—and a simpler case-based reasoning (CBR) approach. We evaluate the impact of the tuning time budget and/or the number of unique problem instances used for training on each of the method’s performance and robustness. Our experiments show that whilst Hydra fails to improve upon the default algorithm configuration, the CBR method can lead to 16% performance increase using as few as 100 training instances. Following these findings, we evaluate Hydra’s methodology when applied to homogenous instance spaces. This analysis shows the limitations of Hydra’s inference mechanisms in these settings and showcases the advantages of distance-based approaches used in CBR.

The benefits of using machine learning approaches in the design, optimisation and understanding of homogeneous catalytic processes are being increasingly realised. We focus on the understanding and implementation of key concepts, which serve as conduits to more advanced chemical machine learning literature, much of which is (presently) outside the area of homogeneous catalysis. Potential pitfalls in the ‘workflow’ procedures needed in the machine learning process are identified and all the examples provided are in a chemical sciences context, including several from ‘real world’ catalyst systems. Finally, potential areas of expansion and impact for machine learning in homogeneous catalysis in the future are considered.

Linear α-olefins (LAOs) are linear alkenes with double bonds at the ends of the molecular chains. LAOs with different chain lengths can be widely applied in various fields. Ethylene oligomerization has become the main process for producing LAOs. In this review, different homogeneous or heterogeneous catalysts recently reported in ethylene oligomerization with Ni, Fe, Co, Cr, etc., as active centers will be discussed. In the homogeneous catalytic system, we mainly discuss the effects of the molecular structure and the electronic and coordination states of complexes on their catalytic activity and selectivity. The Ni, Fe, and Co homogeneous catalysts are discussed separately based on different ligand types, while the Cr-based homogeneous catalysts are discussed separately for ethylene trimerization, tetramerization, and non-selective oligomerization. In heterogeneous catalytic systems, we mainly concentrate on the influence of various supports (metal–organic frameworks, covalent organic frameworks, molecular sieves, etc.) and different ways to introduce active centers to affect the performance in ethylene oligomerization. Finally, a summary and outlook on ethylene oligomerization catalysts are provided based on the current research. The development of highly selective α-olefin formation processes remains a major challenge for academia and industry.

A homogeneous persulfate (PS) activation under low energy consumption ultraviolet light-emitting diodes (UV-LEDs) was established in order to degrade the resistive fluoxetine-hydrochloride drug in aqueous media. For this aim, a falling film photo-reactor, equipped with four arrays of six UV-LEDs (395 nm, 1 W each), was utilized. Trace concentrations of ferrous sulfate and heating the media could significantly improve the degradation efficiency. Under the established optimal conditions of 100 mg/L of persulfate, 1 mg/L of ferrous sulfate, natural pH of 6.4 and temperature of 40 °C, a 71.1% degradation efficiency was achieved for 40 mg/L fluoxetine initial concentration after 50 min. The kinetic study revealed a pseudo-first order overall degradation rate. Based on scavenger quenching results, it was revealed that the hydroxyl radical and sulfate anion radical had, respectively, 52.3 and 31.4% contribution in degradation. To extend results to real conditions, the influence of inorganic cations/anions and also humic acid was investigated. From LC-MS analysis results, the transformation products were identified and three degradation pathways were proposed. Interestingly, a cost evaluation indicated electrical energy consumption of 16.7 kWh/m3 and operating cost of only $1.71 per each cubic meter. Consistently, the profound UV-LED/PS/Fe2+/heat process exhibited desired capabilities in scenarios for treating resistive drug wastewaters.

Abstract Finding an alternative to Cr(VI) as catalyst for the conversion of hypochlorite/hypochlorous acid to chlorate is of critical importance to render the industrial chlorate process safe and sustainable. Recently, telluric acid was identified as a potential replacement but its performance under industrial conditions and its interactions with other parts of the process are still unknown. These factors are elucidated by a combination of density functional theory (DFT) modeling and pilot plant studies. Our results indicate, that the addition of telluric acid indeed has a beneficial effect on the decomposition of HOCl to chlorate. The increased performance, shown as a decreased oxygen formation and an increase in anodic current efficiency. It is mostly related to a buffering effect and an increased selectivity for chlorate formation. Unfortunately, a low cathodic current efficiency was achieved due to reduction of telluric acid to solid Te/TeO2 particles in the electrolyte and at the cathode. Despite the benefits of buffering effects and increased selectivity for chlorate formation, telluric acid is unsuitable as replacement for Cr(VI) in the chlorate process due to lack of compatibility with all process conditions.

Abstract The formation and emission of sulfur trioxide (SO3) in sulfur recovery unit has received increasing attention due to its adverse effects on the operation of plant and environment. Due to the excess oxygen, high concentration of SO2 and high temperature, SO3 formation in the sulfur recovery unit tail gas incinerator may significantly increase. A small horizontal tube reactor was employed to simulate the homogeneous oxidation of SO2 in the tail gas incinerator. The SO3 concentration was measured with a controlled condensation method at the outlet of the reactor. The present work focuses on the gas-phase chemistry and examines the impact of different combustion parameters and atmospheres on the formation of SO3 in the tail gas incinerator. Experiment results show that the increased O2 and SO2 concentrations along with increasing temperature are favorable for enhancing SO3 formation over the range of tested parameters. The presence of water vapor has an enhancing effect of SO2 oxidation in the experiments conducted. No significant effect of CO2 was found to the oxidation of SO2.

Facial expression recognition (FER) plays a crucial role in understanding human emotions and is becoming increasingly relevant in educational contexts, where personalized and empathetic interactions are essential. The problems with existing approaches are typically solved using a single deep learning method, which is not robust with complex datasets, such as FER data, which have a characteristic imbalance and multi-class labels. In this research paper, an innovative approach to FER using a homogeneous ensemble convolutional neural network, called HoE-CNN, is presented for future online learning education. This paper aims to transfer the knowledge of models and FER classification using ensembled homogeneous conventional neural network architectures. FER is challenging to research because there are many real-world applications to consider, such as adaptive user interfaces, games, education, and robot integration. HoE-CNN is used to improve the classification performance on an FER dataset, encompassing seven main multi-classes (Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral). The experiment shows that the proposed framework, which uses an ensemble of deep learning models, performs better than a single deep learning model. In summary, the proposed model will increase the efficiency of FER classification results and solve FER2013 at a accuracy of 75.51%, addressing both imbalanced datasets and multi-class classification to transfer the application of the model to online learning applications.

Abstract Microfluidics has received extensive attention due to its ability to rapidly prepare a large number of microdroplets with controlled sizes and defined morphologies. In addition to having large surface areas and controllable confinement environments, these prepared microdroplets can be used as analytical detection devices to screen and optimize various kinetic parameters. This review summarizes recent advances in the microfluidic control of droplet‐based catalytic reactions and discusses the role of these droplets in both homogeneous and heterogeneous catalyzes and in the catalysis of macromolecular biological enzymes in water‐in‐oil and oil‐in‐oil environments. Additionally, the existing problems and future development directions of droplets in catalysis are highlighted to promote the development of catalytic reactions in droplet media and provide guidance for the high‐throughput screening of catalysts and the directed evolution of biological enzymes.

This research is a large study over an extended period of time involving hundreds of general chemistry students and multiple instructors at the US Naval Academy. The purpose was to investigate whether a combination of clicker content and confidence questions would improve the performance and metacognition of students of different achievement groups (top, middle, bottom). Results showed no statistical difference in exam achievement between students who used clickers and those who did not when other active teaching methods were employed in both groups. Within the clicker participants, statistical differences in confidence on clicker questions were found by student achievement group, question type (algorithmic and conceptual), and question difficulty for both semesters of general chemistry. Students in the top achievement group were the most confident. All students were more confident on algorithmic and easy questions, though a significant interaction was found between question type and difficulty in the second semester. Metacognition was measured by calibration (confidence when the answer is corrector lack of confidence when incorrect). Overall, students of all achievement groups were well calibrated with students in the top group being confident in correct answers and students in the bottom group nonconfident within correct answers. Students in the middle group displayed calibration between those of the top and bottom groups. Evidence of calibration for middle and bottom students may indicate the beginning of metacognitive awareness. A positive trend was found in student perceived value of confidence questions over the two semesters of general chemistry.

Modern nanomaterials and their flow dynamism processes promote complex chemical reactions that are necessary for the accurate synthesis of bespoke geometries at high temperatures. Such flow processes are very intricate and involve viscous behavior along with mass and heat transfer. Such flows mechanism can be controlled by external magnetic fields. Mathematical models offer an inexpensive opening into the fundamental properties of these dynamical processes. The homogeneous-heterogeneous reactions for nanofluids flow are established by invoking the Buongiorno's nanofluid model, in which the homogeneous reactions are regulated by first order kinetics occurring in the flowing liquid and the heterogeneous reactions are given by isothermal cubic autocatalytic kinetics. To testify the feasibility of this model, the steady, laminar Jaffrey-Hamel flow problem in the converging conduit is extended to rheological model. The system steady states are evaluated under the scenario where the reactant and the catalyst's diffusion coefficients are equivalent. In order to investigate heat and mass transfer analysis, viscous dissipation affirmation, Joule heating, and homogeneous-heterogeneous reactions are incorporated. The mathematical model prevailing the dimensionless function, velocity for flow, temperature for heat, and nanoparticles volume fraction for concentration are simulated numerically by means of Runge-Kutta method. The numerical algorithm has been validated in comparison to previously published research with extremely good agreement. The acquisition and detailed discussion of distributions of flow structure, heat, concentrations, and average Nusselt, Sherwood number at a wide range of critical characteristics. The fluid velocity in the conduit center increases significantly as the Reynolds number rises. By intensifying the magnetic field, the flow reversal control is accomplished. Applications in the allied domains have enormous promise since the ratio of Brownian and thermophoretic diffusivity has a significant impact on the transport mechanisms of homogeneous-heterogeneous processes. Chemical species A∗ and B∗ behave in fundamentally distinct ways in the reduced concentrations.

Isotactic poly (1-butene) (iPB) is an interesting semi-crystalline thermoplastic material characterized by notable physical and mechanical attributes encompassing superior creep and stress resistance, elevated toughness, stiffness, and thermal endurance. These distinctive features position iPB as a viable candidate for specific applications; however, its widespread utilization is hindered by certain inherent limitations. Indeed, iPB manifests an intricate polymorphic behavior, and the gradual and spontaneous transition of the kinetically favored form II to the thermodynamically favored form I during aging introduces alterations to the material’s properties. Despite its potential, the attainment of iPB with an exceedingly high molecular mass remains elusive, particularly when employing homogeneous catalysts renowned for their efficacy in propene polymerization. In this study we analyze the mechanistic aspects governing 1-butene polymerization by using DFT calculations modelling the regioselectivity of 1-butene insertions and the termination reactions occurring after primary (1,2) and secondary (2,1) insertions. Finally, the isomerization pathways leading to the formation of 4,1 units in iPB samples synthesized by homogenous catalysts is also discussed. All these aspects, furnish a mechanistic picture of the main drawbacks of an “old” but still interesting material.

This article presents an efficient iron(III) molecular catalyst for the production of biodiesel from waste vegetable oils. The approach involved an initial screening of eight salophen complexes with various substituents on the arene rings, leading to the selection of the simplest unsubstituted species as the most active catalyst. Under optimized conditions, this catalyst demonstrated the capability to achieve complete conversion of the oil at a low catalyst loading (0.10% mol/mol) and convenient conditions (160 °C, 20/1 MeOH/oil ratio).

Abstract The study reports the development of a high-performance liquid chromatography/diode array detection method to measure the levels of nirmatrelvir and ritonavir in human plasma. These two antiviral medications are used for the treatment of COVID-19 and are marketed as Paxlovid®. The method employed sugaring-out induced homogeneous liquid–liquid microextraction to improve sensitivity. Optimization of the method was performed using the one variable at a time approach by adjusting several factors such as type of sugar, extractant, amount of sugar, volume of extractant, and pH of the aqueous sample to achieve the highest efficiency. The developed method was validated according to the Food and Drug Administration guidelines and demonstrated good linearity, accuracy, and precision. The range of linearity was from 1000 to 20,000 ng/mL for nirmatrelvir and 200 to 20,000 ng/mL for ritonavir with correlation coefficient values of 0.998 and 0.996, respectively. Selectivity studies revealed that no others peaks appeared in the retention times of the studied drugs. The stability of nirmatrelvir and ritonavir were also investigated through short term and three cycles of freeze–thaw, and both drugs were found stable. This analytical method could be useful for monitoring drug concentrations in patients undergoing treatment with these medications for COVID-19. In this work, for the first time, SULLME was used for the sensitive determination of nirmatrelvir and ritonavir in biological fluids. The developed method was able to determine both drugs in therapeutic levels with no need to sophisticated techniques like LC–MS. In addition to that, SULLME is considered a simple and green sample preparation in comparison with conventional sample preparation methods.

This paper presents an experimental and numerical evaluation of the pre-chamber induced HCCI combustion concept (PC-HCCI) in terms of engine performance, emissions, and controllability. In this concept, a spark-initiated combustion in the pre-chamber is utilized to trigger the kinetically controlled combustion of an ultra-lean mixture in the main combustion chamber. The experimental measurements were performed on a single-cylinder engine with a custom-made active pre-chamber. A high compression ratio of 17.5 was used, which limits the maximum achievable engine load due to high knocking tendency but enables both standard PCSI combustion (flame propagation) at very high dilution levels and HCCI combustion at reasonable intake temperatures. The analysis of combustion characteristics and the resulting performance is performed at indicated mean effective pressures (IMEPs) of 3.5 and 3.0 bars, and three different intake temperatures of 80 °C, 90 °C, and 100 °C. The variation in engine load was achieved by adjusting the excess air ratio in the main chamber. On each combination of intake temperature and engine load, a spark sweep and an injected PC fuel mass sweep were performed to obtain the highest indicated efficiency while satisfying the restrictions in terms of combustion stability and knock intensity. It was shown that, unlike in a conventional HCCI engine, the combustion phasing can be directly and reliably controlled by adjusting either spark timing or the reactivity of the pre-chamber mixture, ensuring adequate combustion stability and eliminating potential misfires. A similar indicated efficiency as with conventional HCCI combustion was obtained, while the NOx emissions, although slightly elevated, are still insignificant. Compared to PCSI combustion at the same engine load, a 4-percentage-point increase in indicated efficiency and two times lower NOx emissions were achieved. Compared to the most efficient PCSI operating point, it was 1 percentage point lower, indicating that efficiency was achieved, but the specific NOx emissions are reduced by approximately 70%. Most importantly, very similar performance was obtained with significant variations in intake temperature, proving the reliability and adaptability of this combustion concept.

Rate dispersion is commonly observed when measuring relaxation in complex media such as solution of a protein. Its origin is often associated to molecular heterogeneity in the system. However, interpretation of rate dispersion is not straightforward, and it can be exhibited by both heterogeneous (all molecules are different) and homogeneous (all molecules are identical) systems. Here, we report two-dimensional (2D) correlation analysis of intensity fluctuations originating from translational diffusion-lead fluctuations in number of particles simulated in an observation volume resembling fluorescence correlation spectroscopy experiment. Translational diffusion of particles representing a protein having two different conformations are simulated. Homogeneous and heterogeneous systems are simulated to have similar one-dimensional (1D) autocorrelation curves with comparable extent of rate dispersion, α ∼ 0.8. Time slices of 2D autocorrelation curves and rate-rate spectra obtained from them clearly demonstrate that similar extent of rate dispersion observed in 1D autocorrelation can originate from both heterogeneous and homogeneous systems. We establish that one-dimensional analysis only reports on the extent of rate dispersion. Two-dimensional analysis reports on the origin of rate dispersion and distinguishes heterogeneous from homogeneous.

In this account, we discuss the use of genetic algorithms in the inverse design process of homogeneous catalysts for chemical transformations. We describe the main components of evolutionary experiments, specifically the nature of the fitness function to optimize, the library of molecular fragments from which potential catalysts are assembled, and the settings of the genetic algorithm itself. While not exhaustive, this review summarizes the key challenges and characteristics of our own (i.e., NaviCatGA) and other GAs for the discovery of new catalysts.

The current work investigates the influence of porous media, homogeneous and heterogeneous reactions, and a heat source/sink on the hybrid nanoliquid circulation on three distinct surfaces (cone, plate, and wedge). The system of equations that describe the circulation issue and operating conditions is reduced to ordinary differential equations (ODEs) by using the proper similarity transformations. The Runge–Kutta–Fehlberg 45 order and the shooting approach are used to generate the numerical results. Graphs are used to show how various dimensionless limits affect the associated profiles. The results demonstrate that, in the presence of heat source/sink and porous medium characteristics, respectively, fluid velocity and heat dispersion are high in plate geometry and lower in cone geometry. The concentration profile shows the declination in the presence of both homogeneous and heterogeneous reaction intensities. The surface drag force decreases and the rate of heat dispersion rises with the addition of a porous attribute. Furthermore, cones sprinkle the heat more quickly than wedges, which disperse heat more slowly.

The parallelism of rule execution in membrane computing provides support for improving computational efficiency. Membrane computing models have been applied in many fields. In arithmetic operations, designing basic arithmetic operation spiking neural P systems using fewer neurons and rule types has been an important field of membrane computing application research in recent years. We discuss the application of locally homogeneous spiking neural P systems in arithmetic operations. The purpose is to design a spiking neural P system with fewer neurons and rule types to perform arithmetic operations. We designed the addition and subtraction of a locally homogeneous spiking neural P system without weight and delay. They include two input neurons to achieve any two binary number subtraction, one input neuron to achieve any two binary number addition and subtraction, and one input neuron to achieve any n binary number addition and subtraction. This is an attempt to apply the locally homogeneous spiking neural P system in arithmetic operations. Compared with the current excellent spiking neural P system performing arithmetic operations, our designed locally homogeneous spiking neural P system is more concise. The system we designed reduces the number of neurons required for n number addition operations by k − 6 and reduces the number of rule types by 5k − 14.

Intracellular environment includes proteins, sugars, and nucleic acids interacting in restricted media. In the cytoplasm, the excluded volume effect takes up to 40% of the volume available for occupation by macromolecules. In this work, we tested several approaches modeling crowded solutions for protein diffusion. We experimentally showed how the protein diffusion deviates from conventional Brownian motion in artificial conditions modeling the alteration of medium viscosity and rigid spatial obstacles. The studied tracer proteins were globular bovine serum albumin and intrinsically disordered α-casein. Using the pulsed field gradient NMR, we investigated the translational diffusion of protein probes of different structures in homogeneous (glycerol) and heterogeneous (PEG 300/PEG 6000/PEG 40,000) solutions as a function of crowder concentration. Our results showed fundamentally different effects of homogeneous and heterogeneous crowded environments on protein self-diffusion. In addition, the applied “tracer on lattice” model showed that smaller crowding obstacles (PEG 300 and PEG 6000) create a dense net of restrictions noticeably hindering diffusing protein probes, whereas the large-sized PEG 40,000 creates a “less restricted” environment for the diffusive motion of protein molecules.

Driven by recent advances in DNA nanotechnology, analytical methods have been greatly improved for designing simple and homogeneous assays for proteins. The translation from target proteins to DNA outputs dramatically enhances the sensitivity of protein assays. More importantly the protein-responsive DNA nanotechnology has offered diverse assay mechanisms, allowing flexible assay designs and high sensitivity without the need for sophisticated operational procedures. This review will focus on the design principles and mechanistic insight of analytical assays mediated by protein-responsive DNA nanotechnology, which will serve a general guide for assay design and applications. Key words: homogeneous protein assay DNA nanotechnology, DNA nanomachine, protein detection, DNA strand displacement Grace aux recents progres des nanotechnologies ADN, les methodes analytiques ont ete grandement ameliorees pour permettre la conception d'essais simples de dosage homogene des proteines. La traduction de proteines cibles en codes d'ADN ameliore considerablement la sensibilite des dosages des proteines. Fait plus important encore, les nanotechnologies ADN sensibles aux proteines offrent divers mecanismes de dosage, permettant de concevoir des essais flexibles et d'une grande sensibilite sans avoir recours a des manipulations sophistiquees. Le present article de synthese porte sur les principes de conception d'essais analytiques reposant sur les nanotechnologies ADN sensibles aux proteines et les connaissances mecanistiques de leur fonctionnement, qui serviront de guide general pour la conception d'essais et leurs applications. [Traduit par la Redaction] Mots-cles : dosage homogene des proteines, nanotechnologie ADN, nanomachine a base d'ADN, detection des proteines, deplacement de brin d'ADN, Introduction Proteins are an essential class of biomolecules that are involved in numerous bioreactions, biological signaling, and cellular structures. They are also important biomarkers for understanding biological processes and diagnosing [...]

The solid volume fraction of a slurry requiring solid–liquid separation often fluctuates in industrial cake filtration processes. For low solid volume fractions, particle segregation arises, resulting in an inhomogeneous filter cake structure. Particle segregation has significant impacts on cake formation such as a longer cake formation time compared to homogeneous cakes. This work addresses the impact of this effect on vibration compaction, which is an alternative deliquoring method applying oscillatory shears to the filter cake. The dewatering results of homogeneous and segregated cakes made of the same material with a broad particle size distribution are compared. Although cake deliquoring is achievable despite particle segregation, vibration compaction is more effective for homogeneous cakes. The reason is that no particle size homogenization within segregated cakes occurs due to oscillatory shear, as particle size analyses indicate. The particle size measurements of cakes before and after vibration compaction reveal that the material’s particle size distribution is preserved despite vibration application. Vibration compaction achieves higher deliquoring than the common compaction method by squeezing, as elastic recovery effects after squeezing lead to the reabsorbing of liquid, already expressed and stored in the filter cloth. This demonstrates that vibration compaction is a real alternative for cake deliquoring.

In this paper, attention is focused on the perspective of metallic access networks utilizing homogeneous symmetric lines as a transmission medium. This miscellaneous environment has a strong influence on transmitted Digital Subscriber Line (DSL) signals and hence it is necessary to know its specific parameters in the given case. Fast and precise measurements on homogeneous symmetric lines are still essential, meaningful and imperative for many reasons: solving interference problems, designing advanced signal processing techniques and realizing appropriate simulation models. There are various ways of testing measurements with meaningful differences. In this contribution, we are focusing on the importance of measurements on homogeneous symmetric lines by means of metallic ended line testing. Subsequently, possibilities for new evaluation techniques for Metallic Ended Line Testing (MELT) parameters and for analyzing of DSL service provision are presented. Additionally, the realization of a mobile application functionality for facilitating the jobs of technicians is introduced, together with the possible availability of homogeneous symmetric lines for DSL service provision.

Abstract Polycyclic N-heterocycles are important structural motifs commonly found in bioactive compounds, however, their selective construction via the cyclization of allenynes remains challenging yet highly desirable. Here we show a homogeneous copper-catalyzed hetero Diels−Alder (HDA) reaction of allenynes with cis-diazenes (PTAD, 4-phenyl-1,2,4-triazoline-3,5-dione), allowing the practical and efficient synthesis of a diverse array of valuable polycyclic N-heterocycles. A temperature-controlled and stereocontrolled chemoselectivity of the reaction was observed, leading to the chemodivergent synthesis of tetracyclic pyrrolidines, pentacyclic triazepanes and tricyclic pyrrolidines. Compared with related Au-catalyzed cyclization of allenynes, this copper catalysis achieves cyclization of allenynes terminating in C–N bond formation via the HDA reaction.

The indirect effect of radiation plays an important role in radio-induced biological damages. Monte Carlo codes have been widely used in recent years to study the chemical evolution of particle tracks. However, due to the large computational efforts required, their applicability is typically limited to simulations in pure water targets and to temporal scales up to the µs. In this work, a new extension of TRAX-CHEM is presented, namely TRAX-CHEMxt, able to predict the chemical yields at longer times, with the capability of exploring the homogeneous biochemical stage. Based on the species coordinates produced around one track, the set of reaction–diffusion equations is solved numerically with a computationally light approach based on concentration distributions. In the overlapping time scale (500 ns–1 µs), a very good agreement to standard TRAX-CHEM is found, with deviations below 6% for different beam qualities and oxygenations. Moreover, an improvement in the computational speed by more than three orders of magnitude is achieved. The results of this work are also compared with those from another Monte Carlo-based algorithm and a fully homogeneous code (Kinetiscope). TRAX-CHEMxt will allow for studying the variation in chemical endpoints at longer timescales with the introduction, as the next step, of biomolecules, for more realistic assessments of biological response under different radiation and environmental conditions.

The process of storing oil depots and combined station tanks is affected by factors such as process technology, equipment, and management methods. Inevitably, some heavy hydrocarbon components will condense. According to the available literature, the existing detection methods are not enough to accurately measure the component composition so that the proportion of heavy hydrocarbon substances in the lost gas is reduced. In this paper, by inventing a homogeneous reduction device, the lost gas in the entire laboratory process was kept in a homogeneous state so that the gas components were well-retained. Using the homogeneous reduction method and a traditional inspection method, gas chromatography was performed on a standard gas and the on-site lost gas, respectively. The standard gas measurement results show that the mean deviations of the homogeneous reduction method and the traditional test method were −3.45% and −11.62%, respectively, and the reduction degree reached 96.55% with the homogeneous reduction method. The results of the on-site gas loss measurements show that the proportions of most hydrocarbon substances in each lost gas increase to varying degrees after using the homogeneous reduction technology. Therefore, it is proved that these components can be better preserved using the homogeneous reduction method. It can effectively avoid the condensation of components, which is of great significance to the study of oil and gas loss.

Homogeneous freezing of solution droplets is an important pathway of ice formation in the tropopause region. The nucleation rate can be parameterized as a function of water activity, based on empirical fits and some assumptions on the underlying properties of super-cooled water, although a general theory is missing. It is not clear how nucleation events are influenced by the exact formulation of the nucleation rate or even their inherent uncertainty. In this study we investigate the formulation of the nucleation rate of homogeneous freezing of solution droplets (1) to link the formulation to the nucleation rate of pure water droplets, (2) to derive a robust and simple formulation of the nucleation rate, and (3) to determine the impact of variations in the formulation on nucleation events. The nucleation rate can be adjusted, and the formulation can be simplified to a threshold description. We use a state-of-the-art bulk ice microphysics model to investigate nucleation events as driven by constant cooling rates; the key variables are the final ice crystal number concentration and the maximum supersaturation during the event. The nucleation events are sensitive to the slope of the nucleation rate but only weakly affected by changes in its absolute value. This leads to the conclusion that details of the nucleation rate are less important for simulating ice nucleation in bulk models as long as the main feature of the nucleation rate (i.e. its slope) is represented sufficiently well. The weak sensitivity of the absolute values to the nucleation rate suggests that the amount of available solution droplets also does not crucially affect nucleation events. The use of only one distinct nucleation threshold function for analysis and model parameterization should be reinvestigated, since it corresponds to a very high nucleation rate value, which is not reached in many nucleation events with low vertical updrafts. In contrast, the maximum supersaturation and thus the nucleation thresholds reached during an ice nucleation event depend on the vertical updraft velocity or cooling rate. This feature might explain some high supersaturation values during nucleation events in cloud chambers and suggests a reformulation of ice nucleation schemes used in coarse models based on a purely temperature-dependent nucleation threshold.

Abstract Molecular catalysis of electrochemical reactions is a field of intense activity because of the current interest in electrifying chemical transformations, including both electrosynthesis of organic molecules and production of fuels via small molecule activation. Controlled potential electrolysis (CPE) is often coupled with in situ in operando spectroscopic methods with the aim to gather mechanistic information regarding the catalytic species involved. Herein, considering a simple mechanism for a homogeneous molecular catalysis of an electrochemical reaction, we establish the concentration profile of the catalyst in the electrolysis cell enabling to envision the information that can be obtained from the coupling of this CPE with a spectroscopic probe in the cell compartment. We show how the characteristic parameters of the system (catalytic rate constant, cell dimensions and stirring rate) affect the response with particular emphasis on the transition between two limiting cases, namely a ‘fast’ catalysis regime where catalysis only takes place in a small layer adjacent to the electrode surface and a ‘slow’ catalysis regime where catalysis takes place in the bulk of the solution. These formal concepts are then illustrated with an experimental example, the electroreduction of CO2 in dimethylformamide homogeneously catalyzed by iron tetraphenylporphyrin and followed by UV‐vis spectroscopy.

This study examines the effectiveness of parapets in preventing overtopping failures of small-scale homogeneous reservoir dams under seismic loads. In this study, a parapet covered the entire width of the dam crest and was designed to ensure its weight is transmitted to the dam crest. The test included four modes: initial mode, first seepage, seismic, and second seepage. The results show that without parapets the crack length and width expand significantly in the dam crest during the seismic mode, and the effect was large in the second seepage mode. The crack depth increased by 11.3–24 times during the seismic mode and expanded up to 73.3% of the dam height in the longitudinal direction along the axis of the crack formed in the dam crest during the second seepage mode. These findings suggest that the earthquake weakened the dam body, making it vulnerable to penetration. In contrast, the parapet structure effectively suppressed most of the tensile cracks by increasing the constraint force. Additionally, no crack expansion or tearing occurred during the second seepage mode post-earthquake, indicating improved seismic performance and suppression of seepage deformation.

There are plenty of challenges related to the current energy situation [...]

The spike protein receptor-binding domain (RBD) of SARS-CoV-2 is required for the infection of human cells. It is the main target that elicits neutralizing antibodies and also a major component of diagnostic kits. The large demand for this protein has led to the use of plants as a production platform. However, it is necessary to determine the N-glycan structures of an RBD to investigate its efficacy and functionality as a vaccine candidate or diagnostic reagent. Here, we analyzed the N-glycan profile of the RBD produced in rice callus. Of the two potential N-glycan acceptor sites, we found that one was not utilized and the other contained a mixture of complex-type N-glycans. This differs from the heterogeneous mixture of N-glycans found when an RBD is expressed in other hosts, including Nicotiana benthamiana. By comparing the glycosylation profiles of different hosts, we can select platforms that produce RBDs with the most beneficial N-glycan structures for different applications.

We present an overview of fundamental catalytic reactions of nucleophiles with π-systems in relation to gold chemistry. We present examples of reactions with gold-activated π-systems, alkynyl or allenyl moieties, and the regulation of their reactivity due to the presence of an electron-donating or -withdrawing group. The reactions describe furnished hard-to-reach heterocyclic building blocks for medicinal chemistry purposes. Important gold(I) or gold(III) complexes that are used as catalysts are presented. We examine the activation of such π-systems using gold(I) or gold(III) catalysts and the corresponding divergent catalytic transformations. We provide examples of divergent catalysis using gold(I) catalyst and other metal catalysts (Pt, Ag, Pd, Rh, Sc, Cu) or by changing the ligands in gold(I) catalyst complexes. We also discuss the role of the solvent, counterions and additives in gold(I)-catalyzed reactions. We mention, in a few cases, characteristic experimental or computational studies of these gold-catalyzed reactions of nucleophiles with π-systems.

This study explores efficient Sicomet Green (SG) dye degradation using Fe(III)/Lig and Fe(II)/Lig complexes in modified photo-Fenton processes under UV, LED, and sunlight. Sunlight irradiation showed rapid kinetics with over 70% degradation efficiency. Multi-objective grey wolf optimization achieved 98% degradation and 96% mineralization yield using Fe(III)/Lig without pH adjustment. Inorganic ions inhibited the modified photo-Fenton process. A user-friendly app aided predictions and optimal parameters selection, emphasizing natural light sources and pH-neutral medium for dye removal through photo-Fenton.

In the last decade, 3D printing has attracted significant attention and has resulted in benefits to many research areas. Advances in 3D printing with smart materials at the microscale, such as hydrogels and liquid crystalline polymers, have enabled 4D printing and various applications in microrobots, micro-actuators, and tissue engineering. However, the material absorption of the laser power and the aberrations of the laser light spot will introduce a decay in the polymerization degree along the height direction, and the solution to this problem has not been reported yet. In this paper, a compensation strategy for the laser power is proposed to achieve homogeneous and high aspect ratio hydrogel structures at the microscale along the out-of-plane direction. Linear approximations for the power decay curve are adopted for height steps, discretizing the final high aspect ratio structures. The strategy is achieved experimentally with hydrogel structures fabricated by two-photon polymerization. Moreover, characterizations have been conducted to verify the homogeneity of the printed microstructures. Finally, the saturation of material property is investigated by an indirect 3D deformation method. The proposed strategy is proved to be effective and can be explored for other hydrogel materials showing significant deformation. Furthermore, the strategy for out-of-plane variations provides a critical technique to achieve 4D-printed homogeneous shape-adaptive hydrogels for further applications.

Casein (CAS), a typical protein emulsifier, has functional properties limited by its chemical structure in practical production applications. This study aimed to combine phosphatidylcholine (PC) and casein to form a stable complex (CAS/PC) and improve its functional properties through physical modification (homogeneous and ultrasonic treatment). To date, few studies have explored the effects of physical modification on the stability and biological activity of CAS/PC. Interface behavior analysis showed that compared to homogeneous treatment, PC addition and ultrasonic treatment could decrease the mean particle size (130.20 ± 3.96 nm) and increase the zeta potential (−40.13 ± 1.12 mV), indicating the emulsion is more stable. The chemical structural analysis of CAS showed that PC addition and ultrasonic treatment promoted changes in its sulfhydryl content and surface hydrophobicity, exposing more free sulfhydryl groups and hydrophobic binding sites, thereby enhancing its solubility and improving the stability of the emulsion. Additionally, storage stability analysis revealed that the incorporation of PC with ultrasonic treatment could improve the root mean square deviation value and radius of gyration value of CAS. These modifications resulted in an increase the binding free energy between CAS and PC (−238.786 kJ/mol) at 50 °C, leading to an improvement in the thermal stability of the system. Furthermore, digestive behavior analysis indicated that PC addition and ultrasonic treatment could increase the total FFA release from 667.44 ± 22.33 μmol to 1250.33 ± 21.56 μmol. In conclusion, the study underscores the effectiveness of PC addition and ultrasonic treatment in enhancing the stability and bioactivity of CAS, offering novel ideas for designing stable and healthy emulsifiers.

A novel homogeneous label-free electrochemical biosensor using G-triplex/methylene blue (G3/MB) complex as the signal generator together with an amplification assisted by the λ-exonuclease (λ-Exo) has been successfully constructed for ultrasensitive microRNA (miRNA) detection. An integrated microelectrode was designed to realize the miniaturization of the homogeneous electrochemical assay. Taking advantage of G3, that can specifically bind with MB and decrease its diffusion current, a single-stranded functional DNA hairpin structure was designed as the bio-recognition probe. The probe consisted of G3, eight bases to block G3, and the complementary sequences of the target miRNA. Here we chose miRNA141—a potentially diagnostic biomarker of prostate cancer as the model target. The presence of miRNA141 could hybridize with the probe DNA to form a double-stranded structure with a 5′-phosphorylated terminus. Then λ-Exo was adopted to digest mononucleotides from the 5′-end, leading to the release of G3 part and miRNA141. The released miRNA could hybridize with another probe to trigger the cycling process, while the released G3 could therefore interact with MB to cause a detectable decrease of diffusion current. The proposed strategy showed a low detection limit of 16 fM and an excellent specificity to discriminate single-base mismatches. Furthermore, this sensor was applied to detect miRNA141 from diluted human serum samples, indicating that it has great potential in the application of nucleic acid detection in real samples.

Abstract In this study, fast controlled precipitation (FCP) and chronoamperometry (CA) methods were performed to evaluate the influence of the magnetic treatment at 0.70 T, upon the scale formation in synthetic solutions (hardness 25 °F), exposed to a static magnetic field for different exposure times. According to FCP results, homogeneous CaCO3 precipitation rates in treated solution were lower than those in the absence of the magnetic field. It has been found that the memory effect can last up to 24 h after the magnetic treatment even if it decreases over time. As purpose of comparison, CA method was applied to investigate the influence of the magnetic treatment on heterogeneous CaCO3 precipitation. The magnetic field effect increased with the increasing exposure time. The obtained scale, characterized by scanning electronic microscopy and X‐ray diffraction, was formed by a mixture of calcite and aragonite in the presence of magnetic field.

Objective: This work presents an implementation of a stable algorithm that recovers sources located at the boundary separating two homogeneous media in field-programmable gate arrays. Two loop unrolling architectures were developed and analyzed for this purpose. This inverse source problem is ill-posed due to numerical instability, i.e., small errors in the measurement can produce significant changes in the source location. Methodology: To handle the numerical instability when recovering these sources, the Tikhonov regularization method in combination with the Fourier series truncation method are applied in the stable algorithm. This stable algorithm is implemented in two different architectures developed in this work: The first architecture (Mode 1) allows for different operating speeds, which is an advantage depending on whether we work with fast or slow signals. The second one (Mode 2) reduces resource consumption by exploiting the characteristics of the source identification algorithm, which is an advantage for multichannel problems such as inverse electrocardiography or electroencephalography. Results: The architectures were tested on four devices of the 7 Series of Xilinx: Spartan-7 xc7s100fgga484, Virtex-7 xc7v585tffg1157, Kintex-7 xc7k70tfbg484, and Artix-7 xc7a35tcpg236. The two hardware implementations of the stable algorithm were validated using synthetic examples implemented in MATLAB, which shows the advantages of each architecture. Contributions: We developed two efficient architectures based on a loop unrolling design for source identification problems. These are effective strategies to divide and assign tasks to the configurable hardware, and they appear as an appropriate technique for implementing the algorithm. The first one is simple and allows for different operating speeds. The second one uses a control system based on multiplexors that reduce resource consumption and complexity of the design and can be used for multichannel problems. From the numerical test, we found the regularization parameters. The synthetic examples developed here can be considered for similar problems and can be extended to concentric spheres.