g-C3N4 S-Scheme Homojunction through van der Waals Interface Regulation by Intrinsic Polymerization Tailoring for Enhanced Photocatalytic H2 Evolution and CO2 Reduction.

The effective S-scheme homojunction relies on the precise regulation of band structure and construction of advantaged charge migration interfaces. Here, the electronic structural properties of g-C3N4 were modulated through meticulous polymerization of self-assembled supramolecular precursors. Experimental and DFT results indicate that both the intrinsic bandgap and surface electronic characteristics were adjusted, leading to the formation of an in-situ reconstructed homojunction interface facilitated by intrinsic van der Waals forces.

Breaking the intrinsic activity barriers of bilayer metal oxides for catalytic CO reduction.

The photocatalytic CO reduction reaction is severely limited by sluggish charge kinetics. To address this issue, a strategy utilizing non-metal-doped layered double hydroxide (LDH) has been developed to control the electronic structure of spindle-shaped nanoflowers, resulting in efficient photocatalytic CO reduction. The results demonstrate that the designed catalyst yields 263.

Structurally disordered MoSe with rich 1T phase as a universal platform for enhanced photocatalytic hydrogen production.

The improvement of surface reactivity in noble-metal-free cocatalysts is crucial for the development of efficient and cost-effective photocatalytic systems. However, the influence of crystallinity on catalytic efficacy has received limited attention. Herein, we report the utilization of structurally disordered MoSe with abundant 1T phase as a versatile cocatalyst for photocatalytic hydrogen evolution.

Enhancing photocatalytic CO reduction with TiO-based materials: Strategies, mechanisms, challenges, and perspectives.

The concentration of atmospheric CO has exceeded 400 ppm, surpassing its natural variability and raising concerns about uncontrollable shifts in the carbon cycle, leading to significant climate and environmental impacts. A promising method to balance carbon levels and mitigate atmospheric CO rise is through photocatalytic CO reduction. Titanium dioxide (TiO), renowned for its affordability, stability, availability, and eco-friendliness, stands out as an exemplary catalyst in photocatalytic CO reduction.

Soft Sensor Modeling Method for the Marine Lysozyme Fermentation Process Based on ISOA-GPR Weighted Ensemble Learning.

Due to the highly nonlinear, multi-stage, and time-varying characteristics of the marine lysozyme fermentation process, the global soft sensor models established using traditional single modeling methods cannot describe the dynamic characteristics of the entire fermentation process. Therefore, this study proposes a weighted ensemble learning soft sensor modeling method based on an improved seagull optimization algorithm (ISOA) and Gaussian process regression (GPR). First, an improved density peak clustering algorithm (ADPC) was used to divide the sample dataset into multiple local sample subsets.

Adjacent Mn site boosts photocatalytic hydrogen evolution of MnCdS solid solution through a dual-metal-site design.

CdS has emerged as a possible candidate for photocatalytic hydrogen generation. However, further improvement in the performance of the Cd metal site is challenging due to limited optimization space. To solve this limitation, in this work, the Mn-Cd dual-metal photocatalyst was synthesized by a one-step solvothermal method, and the effects of different proportions of bimetals on hydrogen production activity were systematically studied.

Recent Advances of Constructing Metal/Semiconductor Catalysts Designing for Photocatalytic CO Hydrogenation.

With the development of the world economy and the rapid advancement of global industrialization, the demand for energy continues to grow. The significant consumption of fossil fuels, such as oil, coal, and natural gas, has led to excessive carbon dioxide emissions, causing global ecological problems. CO hydrogenation technology can convert CO into high-value chemicals and is considered one of the potential ways to solve the problem of CO emissions.

Light Control-Induced Oxygen Vacancy Generation and In Situ Surface Heterojunction Reconstruction for Boosting CO Reduction.

The weak adsorption of CO and the fast recombination of photogenerated charges harshly restrain the photocatalytic CO reduction efficiency. The simultaneous catalyst design with strong CO capture ability and fast charge separation efficiency is challenging. Herein, taking advantage of the metastable characteristic of oxygen vacancy, amorphous defect BiOCO (named BOC) was built on the surface of defect-rich BiOBr (named BOB) through an in situ surface reconstruction progress, in which the CO in solution reacted with the generated Bi around the oxygen vacancies.

Supercharged CO Photothermal Catalytic Methanation: High Conversion, Rate, and Selectivity.

To overcome the thermodynamic and kinetic impediments of the Sabatier CO methanation reaction, the process must be operated under very high temperature and pressure conditions, to obtain an industrially viable conversion, rate, and selectivity. Herein, we report that these technologically relevant performance metrics have been achieved under much milder conditions using solar rather than thermal energy, where the methanation reaction is enabled by a novel nickel-boron nitride catalyst. In this regard, an in situ generated HOB⋅⋅⋅B surface frustrated Lewis's pair is considered responsible for the high Sabatier conversion 87.

Research on soft sensing method of photosynthetic bacteria fermentation process based on ant colony algorithm and least squares support vector machine.

Photosynthetic bacteria wastewater treatment is an efficient water pollution treatment method, but photosynthetic bacteria fermentation is a multivariable, non-linear, and time-varying process. So it is difficult to establish an accurate model. Aiming at the difficulty of online measurement of key parameters, such as bacterial concentration and matrix concentration in photosynthetic bacteria fermentation process, an improved ant colony algorithm least squares support vector machine (AC-LSSVM) soft sensing model method is proposed in this paper.

Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of .

The problems that the key biomass variables in fermentation process are difficult measure in real time; this paper mainly proposes a multi-model soft sensor modeling method based on the piecewise affine (PWA) modeling method, which is optimized by particle swarm optimization (PSO) with an improved compression factor (ICF). Firstly, the false nearest neighbor method was used to determine the order of the PWA model. Secondly, the ICF-PSO algorithm was proposed to cooperatively optimize the number of PWA models and the parameters of each local model.

A soft sensor model of cell concentration based on IBDA-RELM.

For fermentation process with multi-operating conditions, it is difficult to predict the cell concentration under the new operating conditions by the soft sensor model established under the specific operating conditions. Inspired by the idea of transfer learning, a method based on an improved balanced distribution adaptive regularization extreme learning machine (IBDA-RELM) was proposed to solve the problem. The domain adaptation (DA) method in transfer learning is developed to reduce distribution distance by transforming data.

Unique Dual-Sites Boosting Overall CO Photoconversion by Hierarchical Electron Harvesters.

Low selectivity and poor activity of photocatalytic CO reduction process are usually limiting factors for its applicability. Herein, a hierarchical electron harvesting system is designed on CoNiP hollow nano-millefeuille (CoNiP NH), which enables the charge enrichment on CoNi dual active sites and selective conversion of CO to CH . The CoNiP serves as an electron harvester and photonic "black hole" accelerating the kinetics for CO -catalyzed reactions.

Accelerated Photoreduction of CO to CO over a Stable Heterostructure with a Seamless Interface.

Photocatalytic CO reduction is a means of alleviating energy crisis and environmental deterioration. In this work, a rising two-dimensional (2D) material rarely reported in the field of photocatalytic CO reduction, black phosphorus (BP) nanosheets, is synthesized, on which CoP is in situ grown by solvothermal treatment using BP itself as a P source. CoP on the BP nanosheets (BPs) surface can prevent the destruction of BPs in ambient air and, in the meantime, favor charge separation and CO adsorption and activation during the catalytic process.

A dynamic soft senor modeling method based on MW-ELWPLS in marine alkaline protease fermentation process.

The vital state variables in marine alkaline protease (MP) fermentation are difficult to measure in real-time online, hardly is the optimal control either. In this article, a dynamic soft sensor modeling method which combined just-in-time learning (JITL) technique and ensemble learning is proposed. First, the local weighted partial least squares algorithm (LWPLS) with JITL strategy is used as the basic modeling method.

Soft-sensor modeling for L-lysine fermentation process based on hybrid ICS-MLSSVM.

The L-lysine fermentation process is a complex, nonlinear, dynamic biochemical reaction process with multiple inputs and multiple outputs. There is a complex nonlinear dynamic relationship between each state variable. Some key variables in the fermentation process that directly reflect the quality of the fermentation cannot be measured online in real-time which greatly limits the application of advanced control technology in biochemical processes.

A Non-linear Model Predictive Control Based on Grey-Wolf Optimization Using Least-Square Support Vector Machine for Product Concentration Control in L-Lysine Fermentation.

L-Lysine is produced by a complex non-linear fermentation process. A non-linear model predictive control (NMPC) scheme is proposed to control product concentration in real time for enhancing production. However, product concentration cannot be directly measured in real time.

Modern Soft-Sensing Modeling Methods for Fermentation Processes.

For effective monitoring and control of the fermentation process, an accurate real-time measurement of important variables is necessary. These variables are very hard to measure in real-time due to constraints such as the time-varying, nonlinearity, strong coupling, and complex mechanism of the fermentation process. Constructing soft sensors with outstanding performance and robustness has become a core issue in industrial procedures.

Soft - sensing modeling based on ABC - MLSSVM inversion for marine low - temperature alkaline protease MP fermentation process.

Background: Aiming at the characteristics of nonlinear, multi-parameter, strong coupling and difficulty in direct on-line measurement of key biological parameters of marine low-temperature protease fermentation process, a soft-sensing modeling method based on artificial bee colony (ABC) and multiple least squares support vector machine (MLSSVM) inversion for marine protease fermentation process is proposed.

Methods: Firstly, based on the material balance and the characteristics of the fermentation process, the dynamic "grey box" model of the fed-batch fermentation process of marine protease is established. The inverse model is constructed by analyzing the inverse system existence and introducing the characteristic information of the fermentation process.

Soft-sensing method based on FDLS-SVM in marine alkaline protease fermentation process.

To overcome the problem that soft-sensing model cannot be updated with the bioprocess changes, this article proposed a soft-sensing modeling method which combined fuzzy c-means clustering (FCM) algorithm with least squares support vector machine theory (LS-SVM). FCM is used for separating a whole training data set into several clusters with different centers, each subset is trained by LS-SVM and sub-models are developed to fit different hierarchical property of the process. The new sample data that bring new operation information is introduced in the model, and the fuzzy membership function of the sample to each clustering is first calculated by the FCM algorithm.

The generalized predictive control of bacteria concentration in marine lysozyme fermentation process.

Due to the high degree of strong coupling and nonlinearity of marine lysozyme fermentation process, it is difficult to accurately model the mechanism. In order to achieve real-time online measurement and effective control of bacterial concentration during fermentation, a generalized predictive control method based on least squares support vector machines is proposed. The particle swarm optimization least squares support vector machine (PSO-LS-SVM) model of lysozyme concentration is established by optimizing the regularization parameters and the kernel parameters of the least squares support vector machine by particle swarm optimization.

In Situ Decoration of Zn Cd S with FeP for Efficient Photocatalytic Generation of Hydrogen under Irradiation with Visible Light.

FeP as a noble-metal-free catalyst has been successfully decorated onto the Zn Cd S photocatalyst surface through an in situ phosphating process. In particular, the 2 % FeP/Zn Cd S-P sample showed the best hydrogen generation activity of 24.45 mmol h  g which is over 130 times higher than that of pure Zn Cd S and nearly 1.

Composite of CH NH PbI with Reduced Graphene Oxide as a Highly Efficient and Stable Visible-Light Photocatalyst for Hydrogen Evolution in Aqueous HI Solution.

A facile and efficient photoreduction method is employed to synthesize the composite of methylammonium lead iodide perovskite (MAPbI ) with reduced graphene oxide (rGO). This MAPbI /rGO composite is shown to be an outstanding visible-light photocatalyst for H evolution in aqueous HI solution saturated with MAPbI . Powder samples of MAPbI /rGO (100 mg) show a H evolution rate of 93.

Purification and characterization of catalase from marine bacterium Acinetobacter sp. YS0810.

The catalase from marine bacterium Acinetobacter sp. YS0810 (YS0810CAT) was purified and characterized. Consecutive steps were used to achieve the purified enzyme as follows: ethanol precipitation, DEAE Sepharose ion exchange, Superdex 200 gel filtration, and Resource Q ion exchange.