Liaoning Shihua University

This review focuses on polyhedron-engineered Pt-based nanocrystals as highly active ORR catalysts for PEMFCs.

Since ever-increasing energy demands stimulated intensive research activities on lithium-ion batteries (LIBs), biomass as an earth-abundant renewable energy source has played an intriguing and promising role in developing sustainable biomass-derived carbons and their composite materials for high-performance LIB anodes. Different from other materials (e.g., silicon, tin, metal oxides, etc.), biomass-derived carbons and their composite materials have been applied more and more to LIBs due to their advantages such as low cost, green and eco-friendly synthesis, easy accessibility, sustainable strategy, and improved battery performance, including capacity, cycling property, and stability/durability. This tutorial review focusing on biomass-derived carbons and their composites in the application of LIB anodes will act as a strategic guide to build a close connection between renewable materials and electrochemical energy storage devices. Also, this review provides a critical analysis and comparison of biomass-derived carbons and their composites for LIB anodes, coupled with an important insight into the remaining challenges and future directions in the field.

Band gap-tunable potassium doped graphitic carbon nitride with enhanced mineralization ability was prepared using dicyandiamide monomer and potassium hydrate as precursors. X-ray diffraction (XRD), N2 adsorption, UV-Vis spectroscopy, Fourier transform infrared (FT-IR) spectroscopy, scanning electron microscopy (SEM), photoluminescence (PL) and X-ray photoelectron spectroscopy (XPS) were used to characterize the prepared catalysts. The CB and VB potentials of graphitic carbon nitride could be tuned from -1.09 and +1.56 eV to -0.31 and +2.21 eV by controlling the K concentration. Besides, the addition of potassium inhibited the crystal growth of graphitic carbon nitride, enhanced the surface area and increased the separation rate for photogenerated electrons and holes. The visible-light-driven Rhodamine B (RhB) photodegradation and mineralization performances were significantly improved after potassium doping. A possible influence mechanism of the potassium concentration on the photocatalytic performance was proposed.

Isolating and stabilizing boron Oxidative dehydrogenation of propane can produce propene from shale gas and help to replace petroleum as a propene feedstock. Boron-based catalysts can have high selectivity to propene, but the water by-product can deactivate the catalyst by hydrolyzing boron. Zhou et al. synthesized boron-doped silicate zeolites containing isolated boron sites that were stable against hydrolysis. The catalyst could achieve one-pass propane conversions up to ∼44% with selectivities for propene and >80% for ethene. They observed no deactivation after a 210-hour continuous test. Science , this issue p. 76

Reinforcement learning (RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming (ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively. Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks, showing how they promote ADP formulation significantly. Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has demonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

Li2CO3, Li2O, and LiF are three important inorganic components that build up the “compact” layer of the solid electrolyte interphase which adhere tightly to the graphite anode of lithium ion batteries. The electrical conductivity and the lithium ion diffusivity within this layer are relevant to the rate performance of the graphite anode. Using density functional theory, the electronic structures of the three compounds are calculated and lithium migration dynamics are simulated using nudged elastic band method. Results show that all three components have insulating electronic structures, while lithium vacancies create some strongly localized holes that do not contribute much to the electronic conduction. Lithium diffusion in Li2CO3 and Li2O can be very fast when lithium vacancies are available. The energy barriers of lithium migration in Li2CO3 (ranges from 0.227 to 0.491 eV) and Li2O (0.152 eV) are comparable to that in graphite with the help of vacancies. However, lithium migration in LiF (energy barrier 0.729 eV) is much slower even when there are lithium vacancies in the lattice.

Nowadays, an increasing attention has been paid to the technologies for removing mercury from flue gases. Up to date, no optimal technology that can be broadly applied exists, but the heterogeneous catalytic oxidation of mercury is considered as a promising approach. Based on a brief introduction of the pros and cons of traditional existing technologies, a critical review on the recent advances in heterogeneous catalytic oxidation of elemental mercury is provided. In this contribution, four types of Hg oxidation catalysts including noble metals, selective catalytic reduction (SCR) catalysts, transition metals, and fly ash have been summarized. Both the advantages and disadvantages of these catalysts are described in detail. The influence of various acidic gases including SO2, SO3, NH3, NOx, HCl, Cl2, etc. have been discussed as well. We expect this work will shed light on the development of heterogeneous catalytic oxidation of elemental mercury technology in flue gases, particularly the synthesis of novel and highly efficient Hg0 oxidation catalysts.

Nitrotyrosine is one of the post-translational modifications (PTMs) in proteins that occurs when their tyrosine residue is nitrated. Compared with healthy people, a remarkably increased level of nitrotyrosine is detected in those suffering from rheumatoid arthritis, septic shock, and coeliac disease. Given an uncharacterized protein sequence that contains many tyrosine residues, which one of them can be nitrated and which one cannot? This is a challenging problem, not only directly related to in-depth understanding the PTM's mechanism but also to the nitrotyrosine-based drug development. Particularly, with the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop a high throughput tool in this regard. Here, a new predictor called "iNitro-Tyr" was developed by incorporating the position-specific dipeptide propensity into the general pseudo amino acid composition for discriminating the nitrotyrosine sites from non-nitrotyrosine sites in proteins. It was demonstrated via the rigorous jackknife tests that the new predictor not only can yield higher success rate but also is much more stable and less noisy. A web-server for iNitro-Tyr is accessible to the public at http://app.aporc.org/iNitro-Tyr/. For the convenience of most experimental scientists, we have further provided a protocol of step-by-step guide, by which users can easily get their desired results without the need to follow the complicated mathematics that were presented in this paper just for the integrity of its development process. It has not escaped our notice that the approach presented here can be also used to deal with the other PTM sites in proteins.

Layered gallium telluride (GaTe) has attracted much attention recently, due to its extremely high photoresponsivity, short response time, and promising thermoelectric performance. Different from most commonly studied two-dimensional (2D) materials, GaTe has in-plane anisotropy and a low symmetry with the C2h(3) space group. Investigating the in-plane optical anisotropy, including the electron-photon and electron-phonon interactions of GaTe is essential in realizing its applications in optoelectronics and thermoelectrics. In this work, the anisotropic light-matter interactions in the low-symmetry material GaTe are studied using anisotropic optical extinction and Raman spectroscopies as probes. Our polarized optical extinction spectroscopy reveals the weak anisotropy in optical extinction spectra for visible light of multilayer GaTe. Polarized Raman spectroscopy proves to be sensitive to the crystalline orientation of GaTe, and shows the intricate dependences of Raman anisotropy on flake thickness, photon and phonon energies. Such intricate dependences can be explained by theoretical analyses employing first-principles calculations and group theory. These studies are a crucial step toward the applications of GaTe especially in optoelectronics and thermoelectrics, and provide a general methodology for the study of the anisotropy of light-matter interactions in 2D layered materials with in-plane anisotropy.

Interstitial P doping is more effective in improving the photocatalytic activity compared with substitutional P doping.

Industrial products' reuse, recovery, and recycling are very important because of their environmental and economic benefits. Effective product disassembly planning methods can improve their recovery efficiency and reduce their bad environmental impact. However, the existing approaches pay little attention to sequence-dependent disassembly with resource constraints, such as limited disassembly operators and tools, which makes the current planning methods ineffective in practice. This paper considers a multiobjective resource-constrained and sequence-dependent disassembly optimization problem with disassembly precedence constraints. Energy consumption is adopted to evaluate the disassembly efficiency. Its use with traditional optimization criterion leads to a novel multiobjective optimization model such that the energy consumption and disassembly time are minimized while disassembly profit is maximized. Since the problem complexity increases with the number of components in a product, a lexicographic multiobjective scatter search (SS) method is proposed to solve the proposed multiobjective optimization problem. Its effectiveness is verified by comparing the results of linear weight SS and genetic algorithms. The results show that it is able to provide a better solution in a short execution time and fulfills the precedence requirement in a product structure and resource constraints.

It is well-recognized that obsolete or discarded products can cause serious environmental pollution if they are poorly be handled. They contain reusable resource that can be recycled and used to generate desired economic benefits. Therefore, performing their efficient disassembly is highly important in green manufacturing and sustainable economic development. Their typical examples are electronic appliances and electromechanical/mechanical products. This paper presents a survey on the state of the art of disassembly sequence planning. It can help new researchers or decision makers to search for the right solution for optimal disassembly planning. It reviews the disassembly theory and methods that are applied for the processing, repair, and maintenance of obsolete/discarded products. This paper discusses the recent progress of disassembly sequencing planning in four major aspects: product disassembly modeling methods, mathematical programming methods, artificial intelligence methods, and uncertainty handling. This survey should stimulate readers to be engaged in the research, development and applications of disassembly and remanufacturing methodologies in the Industry 4.0 era.

Disassembly modeling and planning are meaningful and important to the reuse, recovery, and recycling of obsolete and discarded products. However, the existing methods pay little or no attention to resources constraints, e.g., disassembly operators and tools. Thus a resulting plan when being executed may be ineffective in actual product disassembly. This paper proposes to model and optimize selective disassembly sequences subject to multiresource constraints to maximize disassembly profit. Moreover, two scatter search algorithms with different combination operators, namely one with precedence preserved crossover combination operator and another with path-relink combination operator, are designed to solve the proposed model. Their validity is shown by comparing them with the optimization results from well-known optimization software CPLEX for different cases. The experimental results illustrate the effectiveness of the proposed method.

This paper reviews computational-intelligence-involved approaches in active vehicle suspension control systems with a focus on the problems raised in practical implementations by their nonlinear and uncertain properties. After a brief introduction on active suspension models, the paper explores the state of the art in fuzzy inference systems, neural networks, genetic algorithms, and their combination for suspension control issues. Discussions and comments are provided based on the reviewed simulation and experimental results. The paper is concluded with remarks and future directions.

The artificial potential field approach is an efficient path planning method. However, to deal with the local-stable-point problem in complex environments, it needs to modify the potential field and increases the complexity of the algorithm. This study combines improved black-hole potential field and reinforcement learning to solve the problems which are scenarios of local-stable-points. The black-hole potential field is used as the environment in a reinforcement learning algorithm. Agents automatically adapt to the environment and learn how to utilize basic environmental information to find targets. Moreover, trained agents adopt variable environments with the curriculum learning method. Meanwhile, the visualization of the avoidance process demonstrates how agents avoid obstacles and reach the target. Our method is evaluated under static and dynamic experiments. The results show that agents automatically learn how to jump out of local stability points without prior knowledge.

The NH₄⁺ generation rate over the as-prepared ternary metal sulfide catalysts is linearly related to the sulfur vacancy concentration, confirming that the photocatalytic reduction capacity of N₂ over ternary metal sulfides is highly dependent on the amount of sulfur vacancies.

A modified cuckoo search (CS) algorithm is proposed to solve economic dispatch (ED) problems that have nonconvex, non-continuous or non-linear solution spaces considering valve-point effects, prohibited operating zones, transmission losses and ramp rate limits. Comparing with the traditional cuckoo search algorithm, we propose a self-adaptive step size and some neighbor-study strategies to enhance search performance. Moreover, an improved lambda iteration strategy is used to generate new solutions. To show the superiority of the proposed algorithm over several classic algorithms, four systems with different benchmarks are tested. The results show its efficiency to solve economic dispatch problems, especially for large-scale systems.

Wire rod and bar rolling is an important batch production process in steel production systems. A scheduling problem originated from this process is studied in this work by considering the constraints on sequence-dependent family setup time and release time. For each serial batch to be scheduled, it contains several jobs and the number of late jobs within it varies with its start time. First, we model a rolling process using a Petri net (PN), where a so-called rolling transition describes a rolling operation of a batch. The objective of the concerned problem is to determine a firing sequence of all rolling transitions such that the total number of late jobs is minimal. Next, a mixed-integer linear program is formulated based on the PN model. Due to the NP-hardness of the concerned problem, iterated greedy algorithm (IGA)-based methods by using different neighborhood structures and integrating a variable neighborhood descent method are developed to obtain its near-optimal solutions. To test the accuracy, speed, and stability of the proposed algorithms, we compare their solutions of different-size instances with those of CPLEX (a commercial software) and four heuristic peers. The results indicate that the proposed algorithms outperform their peers and have great potential to be applied to industrial production process scheduling. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work deals with a scheduling problem of a batch production process, i.e., wire rod and bar rolling, which is modeled by a Petri net (PN). Due to the NP-hardness of the concerned problem, four iterated greedy algorithm-based methods are developed to solve it. The proposed methods are validated and tested by comparing their solutions with those of four heuristic peers and the exact ones (when available via CPLEX). Extensive experimental results show that they can fast solve one-week-scale instances with better performance than their peers’, thereby proving the readiness to put them in industrial use. When solving a one-month-scale instance, the proposed methods show much better performance than others.

An efficient rhodium-catalyzed method for direct C-H functionalization at the C7 position of a wide range of indoles has been developed. Good to excellent yields of alkenylation products were observed with acrylates, styrenes, and vinyl phenyl sulfones, whereas the saturated alkylation products were obtained in good yield with α,β-unsaturated ketones. Both the N-pivaloyl directing group and the rhodium catalyst proved to be crucial for high regioselectivity and conversion.

Hybrid flow shop scheduling problems have gained an increasing attention in recent years because of its wide applications in real-world production systems. Most of the prior studies assume that the processing time of jobs is deterministic and constant. In practice, jobs' processing time is usually difficult to be exactly known in advance and can be influenced by many factors, e.g., machines' abrasion and jobs' feature, thereby leading to their uncertain and variable processing time. In this paper, a dual-objective stochastic hybrid flow shop deteriorating scheduling problem is presented with the goal to minimize makespan and total tardiness. In the formulated problem, the normal processing time of jobs follows a known stochastic distribution, and their actual processing time is a linear function of their start time. In order to solve it effectively, this paper develops a hybrid multiobjective optimization algorithm that maintains two populations executing the global search in the whole solution space and the local search in promising regions, respectively. An information sharing mechanism and resource allocating method are designed to enhance its exploration and exploitation ability. The simulation experiments are carried out on a set of instances, and several classical algorithms are chosen as its peers for comparison. The results demonstrate that the proposed algorithm has a great advantage in dealing with the investigated problem.