Generation Mechanism Research Articles

Microbubble-enhanced plasma water-based nitrogen fixation: generation mechanisms, regulation strategies, and application prospects

Abstract This study introduces the solar-powered microbubble-enhanced plasma water-based nitrogen fixation (MEPWBNF) technology, a novel and environmentally friendly approach to nitrogen fixation that offers high efficiency. By employing Venturi tubes to produce a continuous flow of microbubbles, this technology significantly enhances the interaction between gaseous plasma nitrogen compounds and water, leading to improved solubility and reaction efficiency of nitrogen compounds in water. Comparative analyses reveal that MEPWBNF achieves a 33.7% reduction in energy consumption and a 52.8% increase in yield compared to traditional PWBNF methods without microbubble-enhanced method. Moreover, the resulting plasma-activated tap water can be directly utilized as a nutrient solution in hydroponic systems, effectively boosting lettuce growth. The adoption of MEPWBNF technology presents a promising strategy for reducing agricultural dependence on chemical fertilizers and advancing sustainable farming practices.

RCDi: Robust Causal Direction Inference Using INUS-Inspired Asymmetry with the Solomonoff Prior

Investigating causal interactions between entities is a crucial task across various scientific domains. The traditional causal discovery methods often assume a predetermined causal direction, which is problematic when prior knowledge is insufficient. Identifying causal directions from observational data remains a key challenge. Causal discovery typically relies on two priors: the uniform prior and the Solomonoff prior. The Solomonoff prior theoretically outperforms the uniform prior in determining causal directions in bivariate scenarios by using the causal independence mechanism assumption. However, this approach has two main issues: it assumes that no unobserved variables affect the outcome, leading to method failure if violated, and it relies on the uncomputable Kolmogorov complexity (KC). In addition, we employ Kolmogorov’s structure function to analyze the use of the minimum description length (MDL) as an approximation for KC, which shows that the function class used for computing the MDL introduces prior biases, increasing the risk of misclassification. Inspired by the insufficient but necessary part of an unnecessary but sufficient condition (INUS condition), we propose an asymmetry where the expected complexity change in the cause, due to changes in the effect, is greater than the reverse. This criterion supplements the causal independence mechanism when its restrictive conditions are not met under the Solomonoff prior. To mitigate prior bias and reduce misclassification risk, we introduce a multilayer perceptron based on the universal approximation theorem as the backbone network, enhancing method stability. Our approach demonstrates a competitive performance against the SOTA methods on the TCEP real dataset. Additionally, the results on synthetic datasets show that our method maintains stability across various data generation mechanisms and noise distributions. This work advances causal direction determination research by addressing the limitations of the existing methods and offering a more robust and stable approach.

Mechanism of Hydrogen Generation Catalyzed by a Single Atom and Its Spin Regulation.

Single-atom catalysts exhibit the excellent catalytic activity and selectivity, making them widely applicable in the fields of advanced materials, environmental science, and chemical synthesis. However, understanding the mechanism of single-atom catalytic reactions, such as the hydrogen generation reaction, is still challenging, which notably hampers the optimization and precise control of the reaction. Here, we immobilize a single-metal atom model catalyst into a single-molecule electrical detection platform for in situ monitoring of the catalytic hydrogen generation process at the single-event level. In combination with theoretical and experimental studies, the catalytic mechanisms of the hydrogen generation reaction, especially the selection of the catalytic center through charge, spin, and orbital quantum control, are elucidated. In addition, a hydrogen generation process via quantum spin-induced catalysis is observed, in which the turnover frequency increases by about 65 times at a magnetic field of 50 mT. This study provides valuable insights into the intrinsic mechanism of single-metal atom catalysis and opens up unique avenues for their precise control, thus offering a useful strategy for efficiently developing clean energy.

Spin-lattice relaxation mechanism of magnetic field effects on singlet fission in amorphous molecular semiconductors.

Singlet fission (SF) in molecular semiconductors is a photophysical process of spontaneous splitting of the excited singlet state into a pair of triplet excitons (TT-pair). This process is usually strongly influenced by spin-selective back geminate TT-annihilation (TTA). Spin selectivity manifests itself in magnetic field effects (MFEs) on both TTA and SF kinetics, the study of which allows us to reveal some specific features of this kinetics. In our work, we analyze the mechanism of MFE generation in TTA and SF processes in amorphous molecular semiconductors. In this mechanism, the MFEs are assumed to be determined by magnetic field dependent spin-lattice relaxation (SLR) in TT-pairs, generated by the zero-field splitting interaction (in T-excitons), fluctuating due to T-exciton hopping over arbitrarily oriented molecules in amorphous semiconductors. The SLR-transitions are described with a semiempirical model, which makes it possible to obtain the SF-kinetic functions in analytical form. The mechanism of SLR-assisted MFEs is found to be very efficient in TTA and SF processes. The obtained results are analyzed in detail and applied to interpret experimentally observed SF-kinetic dependences in various magnetic fields. In particular, it is shown that the proposed model of SLR-generated MFEs enables one to describe the effect of crossing of SF-kinetic functions, corresponding to different magnetic fields.

Structural Colors Go Active.

Structural colors find wide applications for color printing, intelligent display, filtering imaging, etc., owing to their benefits, including high resolution, stable properties, and dynamic tunability. This review first illustrates the mechanisms of structural color generation, such as surface plasmon resonances, localized surface plasmon resonances, Fabry-Perot resonances, Mie resonances, etc. It then proposes the recent technological strategies employed to realize dynamic structural colors. The integration of structural colors with functional materials like phase-change, along with the development of color dynamic control mechanisms such as microfluidic chips, micro-electro-mechanical system drivers, and microheaters, represents key approaches for spectrum regulation. Furthermore, the review assesses the performance, advantages, and limitations of various technologies for dynamic structural colors. Finally, this review concluded with a section on the future challenges and prospects in large-area fabrication, practical applications, and performance improvement. It explains the current typical applications, including smart windows, adaptive camouflage, sensors, etc., and explores the processing methods that can achieve large-area, high-fidelity preparation of structural colors, such as nanoimprint, deep ultraviolet lithography, immersion lithography, laser printing, etc. This field promises advancements in high-density data storage, information encryption, and broader market applications.

Hydrovoltaic Power Generation Depend on Wettability at the Liquid–Solid Interface: Mechanisms, Materials, and Applications With Various Resource

ABSTRACTWith the global increase in energy consumption, there is a growing demand for green energy production, which has prompted the development of novel renewable energy sources. Recently, significant momentum has been observed in research on new energy harvesting methods suitable for small devices. In this context, hydrovoltaic power generation, utilizing water due to its ubiquitous presence and easy availability, has emerged as a promising technology. Hydrovoltaic power generation operates by converting the potential energy of water into electrical energy through the interaction between water and materials capable of inducing an electrical potential gradient. The control of material surface wettability, which determines the interaction with water, plays a crucial role in enhancing the electrical output and long‐term stability of power generation systems. This review categorizes the mechanisms of hydrovoltaic power generation into flow and diffusion mechanisms, discussing respective case studies based on hydrophobic and hydrophilic substrates. Additionally, representative materials used in hydrovoltaic power generation are discussed and the potential to expand this technology across various fields based on the diverse resources of water is demonstrated. The review concludes with future perspectives, highlighting the applications of hydrovoltaic power generation across multiple domains and outlining directions for future research and development.

Transfer models and Standard models for predicting wear rates on the basis of friction noise: A Comparative Study

Abstract Under varying working conditions, friction between polymers and metals leads to surface wear of materials, accompanied by significant energy dissipation, part of which transforms into friction noise. Despite their high nonlinearity, friction noises share certain commonalities in their generation mechanisms. This study proposes a novel transfer mapping model, which, after modeling a specific pair, can predict the behavior of other pairs. We simplify the model through Pearson feature selection and employ decision tree-based algorithms (Decision Tree, Extreme Gradient Boosting, Categorical Boosting) to model the transfer mapping. By comparing the performance of standard models with transfer models, we identify the optimal approach for constructing the transfer model by using Categorical Boosting algorithm.

Based on Silicon-Gel Polymer Electrolyte Dielectric of Low Impedance Tribovoltaic Nanogenerator.

As an innovative renewable energy harvester, the tribovoltaic nanogenerator (TVNG) with lower impedance and DC characteristics has attracted much attention. To alleviate the issues of severe wear for hard-hard semiconductor materials, here, a soft ion dielectric material and semiconductor TVNG based on n-Si/gel polymer electrolyte (GPE) (GPE-TVNG) is proposed. A solid ionic electric double layer (i-EDL) model is established to systematically explore the generation mechanism as well. Moreover, the proposed i-EDL model is verified by subsequent experiments, and the results manifest that the ion directed migration can improve the output performance of GPE-TVNG. The optimized GPE-TVNG has a short-circuit current of 26.5 μA and a low matched impedance of 60 kΩ, which is far lower than the previous reported internal resistance of Si-based TVNG (>100 kΩ). This study broadens the selection of TVNG materials and realizes the effective control of TVNG output.

Evidence for spinal disinhibition as a pain-generating mechanism in fibromyalgia syndrome.

Pain phenomenology in patients with fibromyalgia syndrome (FMS) shows considerable overlap with neuropathic pain. Altered neural processing leading to symptoms of neuropathic pain can occur at the level of the spinal cord, and 1 potential mechanism is spinal disinhibition. A biomarker of spinal disinhibition is impaired H-reflex rate-dependent depression (HRDD). This study investigated whether patients with FMS exhibit evidence of spinal disinhibition. Thirty-one individuals with FMS and 20 healthy volunteers underwent testing of Hoffman reflex including HRDD, along with assessment of clinical signs and symptoms, pressure pain thresholds, temporal summation of pain (wind-up), and conditioned pain modulation (CPM). Small nerve fibre structure was quantified using intraepidermal nerve fibre density and corneal confocal microscopy. Patients with FMS had significantly impaired HRDD at 1 Hz (P = 0.026) and 3 Hz (P = 0.011) and greater wind-up ratio (P = 0.008) compared with healthy controls. Patients with the most impaired HRDD also had the most inefficient CPM but HRDD was not associated with wind-up. Both HRDD and CPM were most impaired in patients with a shorter duration of disease. We demonstrate for the first time that people with FMS show evidence of spinal disinhibition, which is most dominant in shorter duration of disease and may represent a putative mechanism of pain generation in FMS. Identifying people with impairment of central pain processing at an early stage may provide opportunities for targeted mechanistically directed interventions. Longitudinal studies are warranted to tease out the precise contribution of these mechanisms.

Experimental study on the mechanism of biological hydrogen sulfide generation from organic sulfur-rich coal

Experimental study on the mechanism of biological hydrogen sulfide generation from organic sulfur-rich coal

Degradation of floor finishing materials owing to continuous gait: A comprehensive study on the generation and impact of microplastics

With the increasing concern regarding the effects of microplastics (MPs) on human health, it is essential to conduct research on the MPs generated in the atmosphere and on those occurring in indoor environments. One of the causes of particle generation in indoor spaces is the degradation of floor finishing materials, including human gait factor. Therefore, gait simulation experiments were conducted to verify the occurrence of MPs during the gait process. After the experiments, the concentrations of MPs were calculated, and the differences in the concentrations under various gait and floor finishing material conditions were analyzed. Moreover, the morphological analysis of MPs indicates the mechanisms of MPs generation. Additionally, a chemical bond analysis of MPs and surface analysis of floor finishing materials demonstrated the types and sources of MPs generation. Furthermore, the health risk assessment results showed that the health effect of MPs was the lowest in the case of marmoleum flooring, approximately 1.27 and 5.77 times lower than that of laminate flooring and carpet tiles, respectively. Consequently, the study demonstrated that MPs are generated owing to the surface degradation caused by gait and also provided insights into which floor finishing material is least affected by MPs.

Odour prevention strategies in wastewater treatment and composting plants: A review.

Odour emissions from wastewater treatment plants (WWTPs) and composting plants (CPs) have become a critical environmental and public health issue due to the release of complex mixtures of volatile organic compounds, volatile inorganic compounds, and volatile sulphur compounds. These emissions do not only affect ambient air quality but also contribute to nuisance complaints and potential health risks in the nearby communities. This paper provides a comprehensive review of current odour prevention strategies employed in WWTPs and CPs, focusing on both the underlying mechanisms of odour generation and the efficacy of state-of-the-art mitigation techniques. Malodours mitigation approaches including physical, chemical, and biological methods such as the addition of chemical agents, the use of microbial inoculants, the application of adsorbents and bulking agents and the modifications of operational parameters are explored and their performance critically evaluated. By integrating cost-effective odour control strategies into plant design and operational practices, WWTPs and composting facilities can achieve substantial reductions in odour emissions and compliance with stringent environmental regulations, while enhancing relationships with neighbouring communities. Finally, this review underscores the importance of a holistic and multi-disciplinary approach to odour management, combining scientific innovation with practical engineering solutions.

Envelope rotation forest: A novel ensemble learning method for classification

Ensemble learning is an important field and research hotpot of machine learning. However, the existing ensemble learning methods construct subsets by directly sampling the same original sample set. Therefore, the subsets suffer from the low quality of original sample set, such as low separability, low diversity, high sensitivity to noisy samples. As a result, the improvement effect of existing ensemble methods is limited. To address these problems, a new ensemble learning method - envelope rotation forest was proposed. Firstly, envelope sample transformation mechanism (ESTM) is designed to transform the original sample set to multiple layers of envelope sample sets, thereby improving the quality of the sample subsets. The ESTM consists of multilayer iterative mean clustering (MIMC) and interlayer consistency mechanism (ICM). Secondly, the two-dimensional base classifier matrix generation and fusion mechanism (2D_BCMG&FM) is designed to enhance classification performance. The final classification results are obtained by fusing the two-dimensional classification labels. For verification, the proposed method is tested on 26 datasets and is compared with representative methods. It is observed that the accuracy is improved by up to 18.77%, which highlights the significance of considering envelope transformation on original samples for subsequent subset construction.Data and code can be found at: https://github.com/GRabase/ERF

Turbulence model comparison for ventilated cavitation around a hydrofoil

Ventilated cavitation involves complex multiphase flow, phase change, and turbulence, posing challenges for accurate prediction and control. This paper investigates the performance of three turbulence models—Large Eddy Simulation (LES), Detached Eddy Simulation (DES), and Shear Stress Transport (SST)—in predicting the unsteady characteristics and entropy generation mechanisms in ventilated cavitation around a hydrofoil. A comparative analysis with experiments reveals each model's strengths and limitations in capturing cavitation and turbulence at various scales. The results demonstrate that the LES model excels in resolving small-scale turbulent structures, cavitation formation and shedding, as well as reverse flow and vortex dynamics, making it highly suitable for detailed analysis of complex cavitation phenomena. The DES model shows robustness in predicting large-scale flow features but exhibits delays when handling bubble motion near the hydrofoil's trailing edge. The SST model achieves a balance between computational accuracy and efficiency in predicting flow separation and vortex structures, making it ideal for trend analysis in engineering applications. By analyzing boundary vorticity flux lines, Q-criterion, and Omega methods, the study confirms the LES model's superior capability in capturing intricate vortex structures in high-turbulence regions, while also revealing the dynamic evolution of entropy generation during cavitation. Overall, the LES model proves to be the most effective for precise simulation of ventilated cavitation phenomena, while the DES and SST models are better suited for large-scale flow analysis and practical engineering applications, respectively. This research provides theoretical and technical support for the prediction and control of complex ventilated cavitation flows.

Investigation of generation mechanisms in nitrogenous compounds of shale oil using GC-NCD and theoretical calculations

Investigation of generation mechanisms in nitrogenous compounds of shale oil using GC-NCD and theoretical calculations

The time course and organization of hippocampal replay.

The mechanisms by which the brain replays neural activity sequences remain unknown. Recording from large ensembles of hippocampal place cells in freely behaving rats, we observed that replay content is strictly organized over multiple timescales and governed by self-avoidance. After movement cessation, replays avoided the animal's previous path for 3 seconds. Chains of replays avoided self-repetition over a shorter timescale. We used a continuous attractor model of neural activity to demonstrate that neuronal fatigue both generates replay sequences and produces self-avoidance over the observed timescales. In addition, replay of past experience became predominant later into the stopping period, in a manner requiring cortical input. These results indicate a mechanism for replay generation that unexpectedly constrains which sequences can be produced across time.

Targets Selection for Precision Therapy of Relapsed/Refractory Multiple Myeloma: the Latest Advancements.

According to the guidelines, the primary treatment for multiple myeloma is still based on drugs such as carfilzomib, lenalidomide, or daratumumab. However, patients with relapsed/refractory multiple myeloma (RRMM) may be insensitive or develop resistance to the above therapeutic medications. Thus, formulating standardized and rational treatment regimens for such patients remains an area for consideration. Multidrug combinations are available for the therapy of patients with relapsed/refractory multiple myeloma to improve their clinical outcome and prevent the occurrence of multidrug resistance. For instance, combination therapy with immunomodulators, proteasome inhibitors, and CD38 monoclonal antibodies. With the development of genomics and molecular diagnostic technologies, RRMM has entered the era of precision therapy. Targeted immunotherapeutic drugs such as monoclonal antibodies, bispecific antibodies, antibody-drug conjugates (ADCs), and chimeric antigen receptor-T (CAR-T) cells have shown promising clinical response rates and favorable safety profiles in several clinical and experimental studies. These cutting-edge medicinal treatments may provide new hope for a cure for RRMM. However, the choice of treatment regimen still needs to adhere to the principle of individualization. Generally, we recommend treatment with drugs of a new generation or novel mechanism of action for patients with RRMM who are first relapsed, such as next-generation proteasome inhibitors, next-generation immunomodulators, and CD38-based monoclonal antibody regimens.For multiple relapsed RRMM, we recommend choosing a combination regimen or participating in relevant clinical trials. Additionally, monoclonal antibodies have become the standard of care for patients with RRMM. With the introduction of CAR-T therapy, ADCs, and bispecific antibodies, RRMM patients are expected to achieve deep remissions and long-term survival again.

Influence of internal fluid driving mechanisms on red bed bleaching in the Paradox Basin (Colorado Plateau, Utah and Colorado, USA)

While it has been known for some time that reducing fluids have bleached red beds adjacent to fault zones and regionally across the Colorado Plateau, the volumes of fluids expelled along faults have never been quantified. We have developed and applied a suite of one-dimensional hydrologic models to test the hypothesis that internally generated, reducing fluids migrated up sub-basin bounding faults across the Paradox Basin and bleached overlying red beds. The internal fluid driving mechanisms included are mechanical compaction, petroleum and natural gas generation, aquathermal expansion of water, and clay dewatering. The model was calibrated using pressure, temperature, porosity, permeability, and vitrinite reflectance data. Model results indicate that sediment compaction was the most important pressure generation mechanism, producing the majority of internal fluids sourced during basin evolution. Peak fluid migration occurred during the Pennsylvanian−Permian (325−300 Ma) and Cretaceous (95−65 Ma) periods, the latter being concurrent with simulated peak oil/gas generation (87−74 Ma), which likely played a role in the bleaching of red beds. Batch geochemical advection models and mass balance calculations were utilized to estimate the volume of bleaching in an idealized reservoir having a thickness (∼100 m) and porosity (0.2) corresponding to bleached reservoirs observed in the Paradox Basin. Bleaching volume calculations show that internal fluid driving mechanisms were likely responsible for fault-related alteration observed within the Wingate, Morrison, and Navajo Formations in four localities across the Paradox Basin in the Colorado Plateau, Utah and Colorado, USA. The volume calculation required that 33%−55% of the total basinal fluids, composed of hydrogen-sulfide and paleo-seawater, migrated into an overlying red bed reservoir (0.5 wt% Fe2O3).

Self-powered Biomedical Devices: biology, materials, and their interfaces.

Integrating biomedical electronic devices holds profound promise for advancements in healthcare and enhancing individuals' quality of life. However, the persistent challenges associated with the traditional batteries' limited lifespan and bulkiness hinder these devices' long-term functionality and consistent power supply. Here, we delve into the biology and material interfaces in self-powered medical devices by summarizing the intrinsic electric demands in humans, analyzing material and biological mechanisms for electricity generation and storage, and discussing the pathways toward self-chargeable powering. As a result, the current challenges in material designs and biological integrations emerged to shape the future directions in advancing self-powered medical devices. This paper calls on the community to integrate biology and material science to develop self-powering medical devices and improve their clinical prospects.&#xD.

Inhibition of CD36 ameliorates mouse spinal cord injury by accelerating microglial lipophagy.

Spinal cord injury (SCI) is a serious trauma of the central nervous system (CNS). SCI induces a unique lipid-dense environment that results in the deposition of large amounts of lipid droplets (LDs). The presence of LDs has been shown to contribute to the progression of other diseases. Lipophagy, a selective type of autophagy, is involved in intracellular LDs degradation. Fatty acid translocase CD36, a multifunctional transmembrane protein that facilitates the uptake of long-chain fatty acids, is implicated in the progression of certain metabolic diseases, and negatively regulates autophagy. However, the precise mechanisms of LDs generation and degradation in SCI, as well as whether CD36 regulates SCI via lipophagy, remain unknown. In this study, we investigated the role of LDs accumulation in microglia for SCI, as well as the regulatory mechanism of CD36 in microglia lipophagy during LDs elimination in vivo and in vitro. SCI was induced in mice by applying moderate compression on spina cord at T9-T10 level. Locomotion recovery was evaluated at days 0, 1, 3, 7 and 14 following the injury. PA-stimulated BV2 cells wasestablished as the in vitro lipid-loaded model. We observed a marked buildup of LDs in microglial cells at the site of injury post-SCI. More importantly, microglial cells with excessive LDs exhibited elevated activation and stimulated inflammatory response, which drastically triggered the pyroptosis of microglial cells. Furthermore, we found significantly increased CD36 expression, and the breakdown of lipophagy in microglia following SCI. Sulfo-N-succinimidyl oleate sodium (SSO), a CD36 inhibitor, has been shown to promote the lipophagy of microglial cells in SCI mice and PA-treated BV2 cells, which enhanced LDs degradation, ameliorated inflammatory levels and pyroptosis of microglial cells, and ultimately promoted SCI recovery. As expected, inhibition of lipophagy with Baf-A1 reversed the effects of SSO. We conclude that microglial lipophagy is essential for the removal of LDs during SCI recovery. Our research implies that CD36 could be a potential therapeutic target for the treatment and management of SCI.