Atomistic Evidence of Nucleation Mechanism for the Direct Graphite-to-Diamond Transformation

The direct graphite-to-diamond transformation mechanism has been a subject of intense study and remains debated concerning the initial stages of the conversion, the intermediate phases, and their transformation pathways. Here, we successfully recover samples at early conversion stage by tuning high-pressure/high-temperature conditions and reveal direct evidence supporting the nucleation-growth mechanism. Atomistic observations show that intermediate orthorhombic graphite phase mediates the growth of diamond nuclei. Furthermore, we observe that quenchable orthorhombic and rhombohedra graphite are stabilized in buckled graphite at lower temperatures. These intermediate phases are further converted into hexagonal and cubic diamond at higher temperatures following energetically favorable pathways in the order: graphite -> orthorhombic graphite -> hexagonal diamond, graphite -> orthorhombic graphite -> cubic diamond, graphite -> rhombohedra graphite -> cubic diamond. These results significantly improve our understanding of the transformation mechanism, enabling the synthesis of different high-quality forms of diamond from graphite.

GW-DC: A Deep Clustering Model Leveraging Two-Dimensional Image Transformation and Enhancement

Traditional time-series clustering methods usually perform poorly on high-dimensional data. However, image clustering using deep learning methods can complete image annotation and searches in large image databases well. Therefore, this study aimed to propose a deep clustering model named GW_DC to convert one-dimensional time-series into two-dimensional images and improve cluster performance for algorithm users. The proposed GW_DC consisted of three processing stages: the image conversion stage, image enhancement stage, and image clustering stage. In the image conversion stage, the time series were converted into four kinds of two-dimensional images by different algorithms, including grayscale images, recurrence plot images, Markov transition field images, and Gramian Angular Difference Field images; this last one was considered to be the best by comparison. In the image enhancement stage, the signal components of two-dimensional images were extracted and processed by wavelet transform to denoise and enhance texture features. Meanwhile, a deep clustering network, combining convolutional neural networks with K-Means, was designed for well-learning characteristics and clustering according to the aforementioned enhanced images. Finally, six UCR datasets were adopted to assess the performance of models. The results showed that the proposed GW_DC model provided better results.

On the Reliability of Transformerless Photovoltaic DC/AC Converters Based on Mission Profile

Photovoltaic systems are a technology for the generation of electrical energy that is constantly increasing thanks to current technological advances and that contributes to sustainable development. The main stages of photovoltaic systems are the conversion stage, using an inverter, and filtering. These systems may be considered as a mature and growing technology; however, regarding its reliability, there exists some uncertainties, and they are related to the operation, incidents, and its potential failures, due to the number of elements, the environment, and the operating nominal values. For this reason, this article presents a comparative analysis of the reliability of single-phase transformerless photovoltaic inverters used to inject active power into the grid. This evaluation is carried out under the same design specifications for all the inverters analyzed; the study is made using a mission profile considering the IEC TR 62380 standard, where the events and environmental operating conditions are defined, and numerical simulations. This work is aimed at providing suggestions to improve the quality of the photovoltaic system also considering reliability.

A Three-Phase Resonant Boost Inverter Fed Brushless DC Motor Drive for Electric Vehicles

The present article proposes a three-phase resonant boost inverter (TPRBI) to feed a permanent magnet brushless DC (PMBLDC) motor at the requested torque with low ripples due to the sinusoidal current injected into the PMBLDC motor. PMBLDC motors have the highest torque-to-weight ratio compared to other motors and are the best choice for electric vehicle applications. Conventionally, these motors are driven by voltage source inverters (VSI) with trapezoidal current injection, introducing unwanted torque ripples. Moreover, due to the buck operation of VSI, an extra power conversion stage is required to elevate the battery voltage level to desired DC-link voltage. This extra stage increases the number of components used, complexity of control and decreases the efficiency and reliability of the overall system. TPRBI injects sinusoidal current in the PMBLDC motor in the proposed method, thus minimizing the torque ripples. The proposed inverter also has an inherent voltage boost characteristic, thus eliminating the extra power conversion stage. The single-stage conversion from DC to boosted sinusoidal AC enhances the system reliability and efficiency and minimizes the cost and weight of the system. A MATLAB/Simulink model is presented along with simulation results and mathematical validation. A comparative evaluation of the proposed system with the conventional VSI-fed PMBLDC motor is presented in terms of induced torque ripples.

Supercapacitor-Assisted Techniques and Supercapacitor-Assisted Loss Management Concept: New Design Approaches to Change the Roadmap of Power Conversion Systems

All electrical and electronic devices require access to a suitable energy source. In a portable electronic product, such as a cell phone, an energy storage unit drives a complex array of power conversion stages to generate multiple DC voltage rails required. To optimize the overall end-to-end efficiency, these internal power conversions should waste minimal energy and deliver more to the electronic modules. Capacitors are one of the main component families used in electronics, to store and deliver electric charges. Supercapacitors, so called because they provide over a million-fold increase in capacitance relative to a traditional capacitor of the same volume, are enabling a paradigm shift in the design of power electronic converter circuits. Here we show that supercapacitors could function as a lossless voltage-dropping element in the power conversion stages, thereby significantly increasing the power conversion stage efficiency. This approach has numerous secondary benefits: it improves continuity of the supply, suppresses voltage surges, allows the voltage regulation to be electromagnetically silent, and simplifies the design of voltage regulators. The use of supercapacitors allows the development of a novel loss-circumvention theory with applicability to a wide range of supercapacitor-assisted (SCA) techniques. These include low-dropout regulators, transient surge absorbers, LED lighting for DC microgrids, and rapid energy transfer for water heating.

A Novel Automated Design Methodology for Power Electronics Converters

This work proposes a methodology for designing power electronic converters called “Automatic Design for Manufacturing” (ADFM). This methodology proposes creating Power Converter Arrays (PCAs) using standardized converter cells. The approach is greatly inspired by the microelectronics integrated circuit design flow, power electronics building blocks, and multicell converters. To achieve the desired voltage/current specifications, the PCA conversion stage is made from the assembly of several Conversion-Standard Cells (CSCs) in series and/or parallel. The ADFM uses data-based models to simulate the behavior of a PCA with very little computational effort. These models require a special characterization approach to maximize the amount of knowledge while minimizing the amount of data. This approach consists of establishing an experiment plan to select the relevant measurements that contain the most information about the PCA technology, building an experimental setup that is capable of acquiring data automatically and using statistical learning to train models that can yield precise predictions. This work performed over 210 h of tests in nine different PCAs in order to gather data to the statistical models. The models predict the efficiency and converter temperature of several PCAs, and the accuracy is compared with real measurements. Finally, the models are employed to compare the performance of PCAs in a specific battery charging application.

Parallel PV Configuration with Magnetic-Free Switched Capacitor Module-Level Converters for Partial Shading Conditions

In this paper, a module-level photovoltaic (PV) architecture in parallel configuration is introduced for maximum power extraction, under partial shading (PS) conditions. For the first time, a non-regulated switched capacitor (SC) nX converter is a used at the PV-side conversion stage, whose purpose is just to multiply the PV voltage by a fixed ratio and accordingly reduce the input current. All the control functions, including the maximum power point tracking, are transferred to the grid-side inverter. The voltage-multiplied PV modules (VMPVs) are connected in parallel to a common DC-bus, which offers expandability to the system and eliminates the PS issues of a typical string architecture. The advantage of the proposed approach is that the PV-side converter is relieved of bulky capacitors, filters, controllers and voltage/current sensors, allowing for a more compact and efficient conversion stage, compared to conventional per-module systems, such as microinverters. The proposed configuration was initially simulated in a 5 kW residential PV system and compared against conventional PV arrangements. For the experimental validation, a 10X Gallium Nitride (GaN) converter prototype was developed with a flat conversion efficiency of 96.3% throughout the power range. This is particularly advantageous, given the power production variability of PV generators. Subsequently, the VMPV architecture was tested on a two-module 500 WP prototype, exhibiting an excellent power extraction efficiency of over 99.7% under PS conditions and minimal DC-bus voltage variation of 3%, leading to a higher total system efficiency compared to most state-of-the-art configurations.

Removal of Copper, Nickel, and Iron from Lead–Tin Composite by Segregation of Intermetallic Silicon Phases

In the reduction stage of the secondary copper production process, copper, nickel, lead, and tin are collected in a “black copper”, while zinc is volatilized and precipitated as ZnO in the flue dust. The slag coming from this reduction stage is low in valuable metals and is disposed. In the conversion stage, lead and tin are oxidized and incorporated in the slag phase; in accordance with the oxygen potential, this “converter slag” also contains higher contents of copper and nickel. This slag is then reduced in two stages. From the first stage, a copper–nickel metal is returned to the converter stage, and in the second stage, a crude PbSn composite with copper contents of around 10% and nickel contents of around 2.5%, as well as a further usable slag, is obtained from the “secondary” slag. Iron is used as a reducing agent, so that the metal obtained can contain iron of up to over 10%, depending on the reduction duration. The motivation of this investigation is to provide a method for the subsequent refining of the raw PbSn composite, with the aim of obtain a saleable PbSn composite as well as returning the copper and nickel contents quantitatively to the main copper route or to sell them as an alloy. Therefore, the present work aims to investigate the refining of the raw PbSn composite by the separation of the copper, nickel, and iron via the segregation of intermetallic phases. For that, a series of experiments were performed on the formation and subsequent segregation of intermetallic phases, by introduction of an additional element to the system. The results indicated sharper separation of PbSn composite and copper, nickel, and iron due to the higher thermodynamic stability of these phases (selectivity).

NSGA-II-Based Codesign Optimization for Power Conversion and Controller Stages of Interleaved Boost Converters in Electric Vehicle Drivetrains

This article proposes a holistic codesign optimization framework (COF) to simultaneously optimize a power conversion stage and a controller stage using a dual-loop control scheme for multiphase SiC-based DC/DC converters. In this study, the power conversion stage adopts a non-isolated interleaved boost converter (IBC). Besides, the dual-loop control scheme uses type-III controllers for both inner- and outer- loops to regulate the output voltage of the IBC and tackle its non-minimum phase issue. Based on the converter architecture, a multi-objective optimization (MOO) problem including four objective functions (OFs) is properly formulated for the COF. To this end, total input current ripple, total weight of inductors and total power losses are selected as three OFs for the power conversion stage whilst one OF called integral of time-weighted absolute error is considered for the controller stage. The OFs are expressed in analytical forms. To solve the MOO problem, the COF utilizes a non-dominated sorted genetic algorithm (NSGA-II) in combination with an automatic decision-making algorithm to obtain the optimal design solution including the number of phases, switching frequency, inductor size, and the control parameters of type-III controllers. Furthermore, compared to the conventional ‘k-factor’ based controller, the optimal controller exhibits better dynamic responses in terms of undershoot/overshoot and settling time for the output voltage under load disturbances. Moreover, a liquid-cooled SiC-based converter is prototyped and its optimal controller is implemented digitally in dSPACE MicroLabBox. Finally, the experimental results with static and dynamic tests are presented to validate the outcomes of the proposed COF.

Thermal Conversion of Pine Wood and Kinetic Analysis under Oxidative and Non-Oxidative Environments at Low Heating Rate

Atmosphere is one of the most significant factors in the thermal decomposition of biomass. In domestic or industrial biomass boilers, ambient oxygen concentration varies through time, which means that the reaction will change from pyrolysis to combustion. In this way, to analyze and compare each thermochemical conversion process, a simple analytical method, the non-isothermal thermogravimetric analysis, is carried out under oxidative (air) and non-oxidative (argon) environments at 10 °C/min and as a function of different flow rates (2 to 150 mL/min). Additionally, this work was complemented by a kinetic analysis considering a first-order reaction to each conversion stage and using the Coats–Redfern method. The effect of the atmosphere on the thermal decomposition behavior was evident. It was observed that the thermal decomposition of pine wood particles varied from three to two stages when the oxidative or inert atmosphere was applied. The presence of oxygen changes the mass loss curve mainly at high temperature, around 350 °C, where char reacts with oxygen. The maximum mass loss rate from experiments with the oxidative atmosphere is 15% higher than in an inert atmosphere, the average char combustion rate is approximately 5 times higher and the heat released reaches levels 3.44 times higher than in an inert atmosphere. Ignition and combustion indexes were also defined, and results revealed that particles are ignited faster under oxidative atmosphere and that, on average, the combustion index is 1.7 times higher, which reinforces the more vigorous way that the samples are burned and how char is burned out faster in the experiments with air. Regarding the kinetics analysis, higher activation energies, and consequently, lower reactivity was obtained under the oxidative atmosphere for the second stage (~125 kJ/mol) and under the inert atmosphere for the third thermal conversion stage (~190 kJ/mol).