Energy consumption accounts for a significant portion of a product's environmental impacts. Biological research suggests the existence of a fundamental energetic limit. After reviewing research into the influence ...
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Demand for lithium-ion batteries (LIBs) is increasing owing to the expanding use of electrical vehicles and stationary energy storage. Efficient and closed-loop battery recycling strategies are therefore needed, which...
Demand for lithium-ion batteries (LIBs) is increasing owing to the expanding use of electrical vehicles and stationary energy storage. Efficient and closed-loop battery recycling strategies are therefore needed, which will require recovering materials from spent LIBs and reintegrating them into new batteries. In this Review, we outline the current state of LIB recycling, evaluating industrial and developing technologies. Among industrial technologies, pyrometallurgy can be broadly applied to diverse electrode materials but requires operating temperatures of over 1,000 °C and therefore has high energy consumption. Hydrometallurgy can be performed at temperatures below 200 °C and has material recovery rates of up to 93% for lithium, nickel and cobalt, but it produces large amounts of wastewater. Developing technologies such as direct recycling and upcycling aim to increase the efficiency of LIB recycling and rely on improved pretreatment processes with automated disassembly and cleaner mechanical separation. Additionally, the range of materials recovered from spent LIBs is expanding from the cathode materials recycled with established methods to include anode materials, electrolytes, binders, separators and current collectors. Achieving an efficient recycling ecosystem will require collaboration between recyclers, battery manufacturers and electric vehicle manufacturers to aid the design and automation of battery disassembly lines.
The aim of the present study was to perform a detailed morphological analysis of an injectable platelet rich fibrin after combination with two different particulate hydroxyapatite-based granules, a porous zirconia blo...
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The aim of the present study was to perform a detailed morphological analysis of an injectable platelet rich fibrin after combination with two different particulate hydroxyapatite-based granules, a porous zirconia block, and laser-textured zirconia or titanium surfaces. Blood samples were harvested from three participants to prepare the flowable injectable PRF in contact or not with particulate hydroxyapatite (Hap), bone mineral granules (DBBM), porous zirconia blocks, laser-textured titanium or zirconia surfaces. Optical and scanning electron microscopy (SEM) were used to evaluate the fibrin network density, fibrin fibers’ diameter, blood cells, and the interaction of PRF with the biomaterials. Histomorphometry of the flowable PRF was also performed using the hematoxylin–eosin staining protocol. Specimens were independently evaluated by two blinded and well-trained researchers in histomorphometry and microscopy. Particulate Hap and DBBM shown different morphological aspects by SEM analyses since DBBM revealed macro- and micro-scale pores while Hap revealed a dense structure. Hydroxyapatite and DBBM granules were entirely embedded by the fibrin-network in the presence of leukocytes and blood platelets. The zirconia porous structured was filled with PRF and its components. Also, the laser-structured zirconia or implant surfaces were entirely coated with the PRF fibrin network embedding leukocytes and blood platelets. Laser-textured titanium surfaces revealed macro- and micro-scale irregularities that increase the surface area and retention of the injectable PRF. Histomorphometric analyses revealed complementary details on the distribution of lymphocytes, red blood cells, and fibrin associated with platelet aggregation. The flowing and viscosity of an injectable platelet rich fibrin provided an agglomeration of synthetic or xenogeneic particulate bone substitutes and the coating of porous zirconia and textured implant surfaces as inspected by scanning electron microsc
One of the applications of data-driven methods in the industry is the creation of real-time, embedded measurements, whether to monitor or replace sensor signals. As the number of embedded systems in products raises ov...
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ISBN:
(纸本)9781728190495
One of the applications of data-driven methods in the industry is the creation of real-time, embedded measurements, whether to monitor or replace sensor signals. As the number of embedded systems in products raises over time, the energy efficiency of such systems must be considered in the design. The time (processor) efficiency of the embedded software is directly related to the energy efficiency of the embedded system. Therefore, when considering some embedded software solutions, such as data-driven methods, time efficiency must be taken into account to improve energy efficiency. In this work, the energy efficiency of three data-driven methods: the Sparse Identification of Nonlinear Dynamics (SINDy), the Extreme Learning Machine (ELM), and the Random-Vector Functional Link (RVFL) network were assessed by using the creation of a real-time in-cylinder pressure sensor for diesel engines as a task. The three methods were kept with equivalent performances, whereas their relative execution time was tested and classified by their statistical rankings. Additionally, the space (memory) efficiency of the methods was assessed. The contribution of this work is to provide a guide to choose the best data-driven method to be used in an embedded system in terms of efficiency.
Particle swarm optimization (PSO) is a population-based stochastic optimization technique, originally developed by Eberhart and Kennedy, inspired by simulation of a social psychological metaphor instead of the surviva...
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Particle swarm optimization (PSO) is a population-based stochastic optimization technique, originally developed by Eberhart and Kennedy, inspired by simulation of a social psychological metaphor instead of the survival of the fittest individual. In PSO, the system (swarm) is initialized with a population of random solutions (particles) and searches for optima using cognitive and social factors by updating generations. PSO has been successfully applied to a wide range of applications, mainly in solving continuous nonlinear optimization problems. Based on the PSO and chaos theories, this paper discusses the use of a chaotic PSO approach hybridized with an implicit filtering (IF) technique to optimize performance of economic dispatch problems. The chaotic PSO with chaos sequences is the global optimizer and the IF is used to fine-tune the chaotic PSO run in sequential manner. The hybrid methodology is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects.
This paper is focused on investigating force regulation strategies employed by human central nervous system (CNS). The mechanism responsible for force control is extremely important in people's lives, but not yet ...
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ISBN:
(纸本)9781424441211
This paper is focused on investigating force regulation strategies employed by human central nervous system (CNS). The mechanism responsible for force control is extremely important in people's lives, but not yet well understood. We formulate the general model of force regulation and identify several possible control strategies. An experimental approach is used to determine which of the force control strategies could actually be used by the CNS. Obtained results suggest that the force regulation process involves not only the pure force controller, but also a coupled motion controller, relying on the internal model of the environment.
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