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1.
Nature ; 629(8012): 507, 2024 May.
Artigo em Inglês | MEDLINE | ID: mdl-38714907
2.
PLoS One ; 19(5): e0302972, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38722925

RESUMO

Electroless nickel plating is a suitable technology for the hydrogen industry because electroless nickel can be mass-produced at a low cost. Investigating in a complex environment where hydrogen permeation and friction/wear work simultaneously is necessary to apply it to hydrogen valves for hydrogen fuel cell vehicles. In this research, the effects of hydrogen permeation on the mechanical characteristics of electroless nickel-plated free-cutting steel (SUM 24L) were investigated. Due to the inherent characteristics of electroless nickel plating, the damage (cracks and delamination of grain) and micro-particles by hydrogen permeation were clearly observed at the grain boundaries and triple junctions. In particular, the cracks grew from grain boundary toward the intergranualr. This is because the grain boundaries and triple junctions are hydrogen permeation pathways and increasing area of the hydrogen partial pressure. As a result, its surface roughness increased by a maximum of two times, and its hardness and adhesion strength decreased by hydrogen permeation. In particular, hydrogen permeation increased the friction coefficient of the electroless nickel-plated layer, and the damage caused by adhesive wear was significantly greater, increasing the wear depth by up to 5.7 times. This is believed to be due to the decreasing in wear resistance of the electroless nickel plating layer damaged by hydrogen permeation. Nevertheless, the Vickers hardness and the friction coefficient of the electroless nickel plating layer were improved by about 3 and 5.6 times, respectively, compared with those of the free-cutting steel. In particular, the electroless nickel-plated specimens with hydrogen embrittlement exhibited significantly better mechanical characteristics and wear resistance than the free-cutting steel.


Assuntos
Hidrogênio , Níquel , Aço , Hidrogênio/química , Níquel/química , Aço/química , Fontes de Energia Elétrica , Propriedades de Superfície , Teste de Materiais
3.
PLoS One ; 19(5): e0300145, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38743740

RESUMO

Integration of renewable energy sources (RES) to the grid in today's electrical system is being encouraged to meet the increase in demand of electrical power and also overcome the environmental related problems by reducing the usage of fossil fuels. Power Quality (PQ) is a critical problem that could have an effect on utilities and consumers. PQ issues in the modern electric power system were turned on by a linkage of RES, smart grid technologies and widespread usage of power electronics equipment. Unified Power Quality Conditioner (UPQC) is widely employed for solving issues with the distribution grid caused by anomalous voltage, current, or frequency. To enhance UPQC performance, Fractional Order Proportional Integral Derivative (FOPID) is developed; nevertheless, a number of tuning parameters restricts its performance. The best solution for the FOPID controller problem is found by using a Coati Optimization Algorithm (COA) and Osprey Optimization Algorithm (OOA) are combined to make a hybrid optimization CO-OA algorithm approach to mitigate these problems. This paper proposes an improved FOPID controller to reduce PQ problems while taking load power into account. In the suggested model, a RES is connected to the grid system to supply the necessary load demand during the PQ problems period. Through the use of an enhanced FOPID controller, both current and voltage PQ concerns are separately modified. The pulse signal of UPQC was done using the optimal controller, which analyzes the error value of reference value and actual value to generate pulses. The integrated design mitigates PQ issues in a system at non-linear load and linear load conditions. The proposed model provides THD of 12.15% and 0.82% at the sag period, 10.18% and 0.48% at the swell period, and 10.07% and 1.01% at the interruption period of non-linear load condition. A comparison between the FOPID controller and the traditional PI controller was additionally taken. The results showed that the recommended improved FOPID controller for UPQC has been successful in reducing the PQ challenges in the grid-connected RESs system.


Assuntos
Algoritmos , Energia Renovável , Fontes de Energia Elétrica , Modelos Teóricos , Eletricidade
4.
PLoS One ; 19(5): e0303207, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38728355

RESUMO

This paper introduces a novel and improved double-resistor damped double-tuned passive power filter (DR-DDTF), designed using multi-objective optimization algorithms to mitigate harmonics and increase the hosting capacity of distribution systems with distributed energy resources. Although four different topologies of single-resistor damped double-tuned filters (DDTFs) have been studied before in the literature, the effectiveness of two different DR-DDTF configurations has not been examined. This work redresses this gap by demonstrating that via comprehensive simulations on two power systems, DR-DDTF provides better harmonic suppression and resonance mitigation than single-resistor alternatives. When it comes to optimizing the DR-DDTF for maximum hosting capacity and minimum system active power losses, the multi-objective artificial hummingbird outperformed six other algorithms in the benchmark. To allow for higher penetration of distributed generation without requiring grid upgrades, this newly developed harmonic mitigation filter provides a good alternative.


Assuntos
Algoritmos , Animais , Aves/fisiologia , Fontes de Energia Elétrica , Simulação por Computador , Modelos Teóricos
5.
PLoS One ; 19(4): e0297068, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38593127

RESUMO

Compared with visible light images, thermal infrared images have poor resolution, low contrast, signal-to-noise ratio, blurred visual effects, and less information. Thermal infrared sports target detection methods relying on traditional convolutional networks capture the rich semantics in high-level features but blur the spatial details. The differences in physical information content and spatial distribution of high and low features are ignored, resulting in a mismatch between the region of interest and the target. To address these issues, we propose a local attention-guided Swin-transformer thermal infrared sports object detection method (LAGSwin) to encode sports objects' spatial transformation and orientation information. On the one hand, Swin-transformer guided by local attention is adopted to enrich the semantic knowledge of low-level features by embedding local focus from high-level features and generating high-quality anchors while increasing the embedding of contextual information. On the other hand, an active rotation filter is employed to encode orientation information, resulting in orientation-sensitive and invariant features to reduce the inconsistency between classification and localization regression. A bidirectional criss-cross fusion strategy is adopted in the feature fusion stage to enable better interaction and embedding features of different resolutions. At last, the evaluation and verification of multiple open-source sports target datasets prove that the proposed LAGSwin detection framework has good robustness and generalization ability.


Assuntos
Fontes de Energia Elétrica , Exame Físico , Generalização Psicológica , Conhecimento , Luz
6.
Int J Med Robot ; 20(2): e2632, 2024 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-38630888

RESUMO

BACKGROUND: Real-time prediction of the remaining surgery duration (RSD) is important for optimal scheduling of resources in the operating room. METHODS: We focus on the intraoperative prediction of RSD from laparoscopic video. An extensive evaluation of seven common deep learning models, a proposed one based on the Transformer architecture (TransLocal) and four baseline approaches, is presented. The proposed pipeline includes a CNN-LSTM for feature extraction from salient regions within short video segments and a Transformer with local attention mechanisms. RESULTS: Using the Cholec80 dataset, TransLocal yielded the best performance (mean absolute error (MAE) = 7.1 min). For long and short surgeries, the MAE was 10.6 and 4.4 min, respectively. Thirty minutes before the end of surgery MAE = 6.2 min, 7.2 and 5.5 min for all long and short surgeries, respectively. CONCLUSIONS: The proposed technique achieves state-of-the-art results. In the future, we aim to incorporate intraoperative indicators and pre-operative data.


Assuntos
Laparoscopia , Humanos , Salas Cirúrgicas , Fontes de Energia Elétrica
7.
PLoS One ; 19(4): e0301019, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38573957

RESUMO

Automatic and accurate segmentation of medical images plays an essential role in disease diagnosis and treatment planning. Convolution neural networks have achieved remarkable results in medical image segmentation in the past decade. Meanwhile, deep learning models based on Transformer architecture also succeeded tremendously in this domain. However, due to the ambiguity of the medical image boundary and the high complexity of physical organization structures, implementing effective structure extraction and accurate segmentation remains a problem requiring a solution. In this paper, we propose a novel Dual Encoder Network named DECTNet to alleviate this problem. Specifically, the DECTNet embraces four components, which are a convolution-based encoder, a Transformer-based encoder, a feature fusion decoder, and a deep supervision module. The convolutional structure encoder can extract fine spatial contextual details in images. Meanwhile, the Transformer structure encoder is designed using a hierarchical Swin Transformer architecture to model global contextual information. The novel feature fusion decoder integrates the multi-scale representation from two encoders and selects features that focus on segmentation tasks by channel attention mechanism. Further, a deep supervision module is used to accelerate the convergence of the proposed method. Extensive experiments demonstrate that, compared to the other seven models, the proposed method achieves state-of-the-art results on four segmentation tasks: skin lesion segmentation, polyp segmentation, Covid-19 lesion segmentation, and MRI cardiac segmentation.


Assuntos
COVID-19 , Exame Físico , Humanos , Fontes de Energia Elétrica , Coração , Redes Neurais de Computação , Processamento de Imagem Assistida por Computador
8.
Waste Manag ; 180: 96-105, 2024 May 15.
Artigo em Inglês | MEDLINE | ID: mdl-38564915

RESUMO

The growing electric vehicle industry has increased the demand for raw materials used in lithium-ion batteries (LIBs), raising concerns about material availability. Froth flotation has gained attention as a LIB recycling method, allowing the recovery of low value materials while preserving the chemical integrity of electrode materials. Furthermore, as new battery chemistries such as lithium titanate (LTO) are introduced into the market, strategies to treat mixed battery streams are needed. In this work, laboratory-scale flotation separation experiments were conducted on two model black mass samples: i) a mixture containing a single cathode (i.e., NMC811) and two anode species (i.e., LTO and graphite), simulating a mixed feedstock prior to hydrometallurgical treatment; and ii) a graphite-TiO2 mixture to reflect the expected products after leaching. The results indicate that graphite can be recovered with > 98 % grade from NMC811-LTO-graphite mixtures. Additionally, it was found that flotation kinetics are dependent on the electrode particle species present in the suspension. In contrast, the flotation of graphite from TiO2 resulted in a low grade product (<96 %) attributed to the significant entrainment of ultrafine TiO2 particles. These results suggest that flotation of graphite should be preferably carried out before hydrometallurgical treatment of black mass.


Assuntos
Grafite , Lítio , Reciclagem/métodos , Fontes de Energia Elétrica , Íons
9.
J Environ Manage ; 357: 120774, 2024 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-38569265

RESUMO

The booming electric vehicle market has led to an increasing number of end-of-life power batteries. In order to reduce environmental pollution and promote the realization of circular economy, how to fully and effectively recycle the end-of-life power batteries has become an urgent challenge to be solved today. The recycling & remanufacturing center is an extremely important and key facility in the recycling process of used batteries, which ensures that the recycled batteries can be handled in a standardized manner under the conditions of professional facilities. In reality, different adjustment options for existing recycling & remanufacturing centers have a huge impact on the planning of new sites. This paper proposes a mixed-integer linear programming model for the siting problem of battery recycling & remanufacturing centers considering site location-adjustment. The model allows for demolition, renewal, and new construction options in planning for recycling & remanufacturing centers. By adjusting existing sites, this paper provides an efficient allocation of resources under the condition of meeting the demand for recycling of used batteries. Next, under the new model proposed in this paper, the uncertainty of the quantity and capacity of recycled used batteries is considered. By establishing different capacity conditions of batteries under multiple scenarios, a robust model was developed to determine the number and location of recycling & remanufacturing centers, which promotes sustainable development, reduces environmental pollution and effectively copes with the risk of the future quantity of used batteries exceeding expectations. In the final results of the case analysis, our proposed model considering the existing sites adjustment reduces the cost by 3.14% compared to the traditional model, and the average site utilization rate is 15.38% higher than the traditional model. The results show that the model has an effective effect in reducing costs, allocating resources, and improving efficiency, which could provide important support for decision-making in the recycling of used power batteries.


Assuntos
Fontes de Energia Elétrica , Reciclagem , Incerteza , Reciclagem/métodos , Poluição Ambiental , Eletricidade
10.
PLoS One ; 19(4): e0301516, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38568998

RESUMO

The integration of renewable energy systems into electricity grids is a solution for strengthening electricity distribution networks (SEDNs). Renewable energies such as solar photovoltaics are suitable for reinforcing a low-voltage line by offering an electrical energy storage system. However, the integration of photovoltaic systems can lead to problems of harmonic distortion due to the presence of direct current or non-linear feedback in networks from other sources. Therefore, connection standards exist to ensure the quality of the energy before injection at a point of common coupling (PCC). In this work, particle swarm optimization (PSO) is used to control a boost converter and to evaluate the power losses and the harmonic distortion rate. The test on the IEEE 14 bus standard makes it possible to determine the allocation or integration nodes for other sources such as biomass, wind or hydrogen generators, in order to limit the impact of harmonic disturbances (LIHs). The evaluation of the harmonic distortion rate, the power losses as well as the determination of the system size is done using an objective function defined based on the integration and optimization constraints of the system. The proposed model performs better since the grid current and voltage are stabilized in phase after the photovoltaic source is injected.


Assuntos
Fontes de Energia Elétrica , Modelos Teóricos , Algoritmos , Energia Renovável , Eletricidade
11.
J Neural Eng ; 21(2)2024 Apr 09.
Artigo em Inglês | MEDLINE | ID: mdl-38565124

RESUMO

Objective.Recent studies have shown that integrating inertial measurement unit (IMU) signals with surface electromyographic (sEMG) can greatly improve hand gesture recognition (HGR) performance in applications such as prosthetic control and rehabilitation training. However, current deep learning models for multimodal HGR encounter difficulties in invasive modal fusion, complex feature extraction from heterogeneous signals, and limited inter-subject model generalization. To address these challenges, this study aims to develop an end-to-end and inter-subject transferable model that utilizes non-invasively fused sEMG and acceleration (ACC) data.Approach.The proposed non-invasive modal fusion-transformer (NIMFT) model utilizes 1D-convolutional neural networks-based patch embedding for local information extraction and employs a multi-head cross-attention (MCA) mechanism to non-invasively integrate sEMG and ACC signals, stabilizing the variability induced by sEMG. The proposed architecture undergoes detailed ablation studies after hyperparameter tuning. Transfer learning is employed by fine-tuning a pre-trained model on new subject and a comparative analysis is performed between the fine-tuning and subject-specific model. Additionally, the performance of NIMFT is compared to state-of-the-art fusion models.Main results.The NIMFT model achieved recognition accuracies of 93.91%, 91.02%, and 95.56% on the three action sets in the Ninapro DB2 dataset. The proposed embedding method and MCA outperformed the traditional invasive modal fusion transformer by 2.01% (embedding) and 1.23% (fusion), respectively. In comparison to subject-specific models, the fine-tuning model exhibited the highest average accuracy improvement of 2.26%, achieving a final accuracy of 96.13%. Moreover, the NIMFT model demonstrated superiority in terms of accuracy, recall, precision, and F1-score compared to the latest modal fusion models with similar model scale.Significance.The NIMFT is a novel end-to-end HGR model, utilizes a non-invasive MCA mechanism to integrate long-range intermodal information effectively. Compared to recent modal fusion models, it demonstrates superior performance in inter-subject experiments and offers higher training efficiency and accuracy levels through transfer learning than subject-specific approaches.


Assuntos
Gestos , Reconhecimento Psicológico , Rememoração Mental , Fontes de Energia Elétrica , Redes Neurais de Computação , Eletromiografia
12.
Sci Rep ; 14(1): 7626, 2024 04 01.
Artigo em Inglês | MEDLINE | ID: mdl-38561445

RESUMO

This study explored the application of generative pre-trained transformer (GPT) agents based on medical guidelines using large language model (LLM) technology for traumatic brain injury (TBI) rehabilitation-related questions. To assess the effectiveness of multiple agents (GPT-agents) created using GPT-4, a comparison was conducted using direct GPT-4 as the control group (GPT-4). The GPT-agents comprised multiple agents with distinct functions, including "Medical Guideline Classification", "Question Retrieval", "Matching Evaluation", "Intelligent Question Answering (QA)", and "Results Evaluation and Source Citation". Brain rehabilitation questions were selected from the doctor-patient Q&A database for assessment. The primary endpoint was a better answer. The secondary endpoints were accuracy, completeness, explainability, and empathy. Thirty questions were answered; overall GPT-agents took substantially longer and more words to respond than GPT-4 (time: 54.05 vs. 9.66 s, words: 371 vs. 57). However, GPT-agents provided superior answers in more cases compared to GPT-4 (66.7 vs. 33.3%). GPT-Agents surpassed GPT-4 in accuracy evaluation (3.8 ± 1.02 vs. 3.2 ± 0.96, p = 0.0234). No difference in incomplete answers was found (2 ± 0.87 vs. 1.7 ± 0.79, p = 0.213). However, in terms of explainability (2.79 ± 0.45 vs. 07 ± 0.52, p < 0.001) and empathy (2.63 ± 0.57 vs. 1.08 ± 0.51, p < 0.001) evaluation, the GPT-agents performed notably better. Based on medical guidelines, GPT-agents enhanced the accuracy and empathy of responses to TBI rehabilitation questions. This study provides guideline references and demonstrates improved clinical explainability. However, further validation through multicenter trials in a clinical setting is necessary. This study offers practical insights and establishes groundwork for the potential theoretical integration of LLM-agents medicine.


Assuntos
Lesões Encefálicas Traumáticas , Humanos , Lesões Encefálicas Traumáticas/tratamento farmacológico , Encéfalo , Bases de Dados Factuais , Fontes de Energia Elétrica , Empatia
13.
Sci Adv ; 10(15): eadn0858, 2024 Apr 12.
Artigo em Inglês | MEDLINE | ID: mdl-38608028

RESUMO

Miniaturized neuromodulation systems could improve the safety and reduce the invasiveness of bioelectronic neuromodulation. However, as implantable bioelectronic devices are made smaller, it becomes difficult to store enough power for long-term operation in batteries. Here, we present a battery-free epidural cortical stimulator that is only 9 millimeters in width yet can safely receive enough wireless power using magnetoelectric antennas to deliver 14.5-volt stimulation bursts, which enables it to stimulate cortical activity on-demand through the dura. The device has digitally programmable stimulation output and centimeter-scale alignment tolerances when powered by an external transmitter. We demonstrate that this device has enough power and reliability for real-world operation by showing acute motor cortex activation in human patients and reliable chronic motor cortex activation for 30 days in a porcine model. This platform opens the possibility of simple surgical procedures for precise neuromodulation.


Assuntos
Fontes de Energia Elétrica , Córtex Motor , Humanos , Animais , Suínos , Reprodutibilidade dos Testes
14.
Sensors (Basel) ; 24(7)2024 Mar 28.
Artigo em Inglês | MEDLINE | ID: mdl-38610372

RESUMO

The build-up of lactate in solid tumors stands as a crucial and early occurrence in malignancy development, and the concentration of lactate in the tumor microenvironment may be a more sensitive indicator for analyzing primary tumors. In this study, we designed a self-powered lactate sensor for the rapid analysis of tumor samples, utilizing the coupling between the piezoelectric effect and enzymatic reaction. This lactate sensor is fabricated using a ZnO nanowire array modified with lactate oxidase (LOx). The sensing process does not require an external power source or batteries. The device can directly output electric signals containing lactate concentration information when subjected to external forces. The lactate concentration detection upper limit of the sensor is at least 27 mM, with a limit of detection (LOD) of approximately 1.3 mM and a response time of around 10 s. This study innovatively applied self-powered technology to the in situ detection of the tumor microenvironment and used the results to estimate the growth period of the primary tumor. The availability of this application has been confirmed through biological experiments. Furthermore, the sensor data generated by the device offer valuable insights for evaluating the likelihood of remote tumor metastasis. This study may expand the research scope of self-powered technology in the field of medical diagnosis and offer a novel perspective on cancer diagnosis.


Assuntos
Nanofios , Neoplasias , Humanos , Ácido Láctico , Neoplasias/diagnóstico , Fontes de Energia Elétrica , Eletricidade , Microambiente Tumoral
15.
Int J Mol Sci ; 25(7)2024 Mar 28.
Artigo em Inglês | MEDLINE | ID: mdl-38612602

RESUMO

Molecular property prediction is an important task in drug discovery, and with help of self-supervised learning methods, the performance of molecular property prediction could be improved by utilizing large-scale unlabeled dataset. In this paper, we propose a triple generative self-supervised learning method for molecular property prediction, called TGSS. Three encoders including a bi-directional long short-term memory recurrent neural network (BiLSTM), a Transformer, and a graph attention network (GAT) are used in pre-training the model using molecular sequence and graph structure data to extract molecular features. The variational auto encoder (VAE) is used for reconstructing features from the three models. In the downstream task, in order to balance the information between different molecular features, a feature fusion module is added to assign different weights to each feature. In addition, to improve the interpretability of the model, atomic similarity heat maps were introduced to demonstrate the effectiveness and rationality of molecular feature extraction. We demonstrate the accuracy of the proposed method on chemical and biological benchmark datasets by comparative experiments.


Assuntos
Benchmarking , Descoberta de Drogas , Animais , Fontes de Energia Elétrica , Estro , Aprendizado de Máquina Supervisionado
16.
PLoS One ; 19(4): e0301910, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38635672

RESUMO

With the increasing demand for electricity, microgrid systems are facing issues such as insufficient backup capacity, frequent load switching, and frequent malfunctions, making research on microgrid resilience crucial, especially to improve system power supply reliability. This paper proposes a method for analyzing the resilience metric of new energy grid-connected microgrid system, and proposes optimization strategies to improve resilience. Firstly, a measurement method for the resilience of the microgrid system is established based on the operating characteristics of the system components. Secondly, the sensitivity relationship between system resilience and parameters is established, and an optimization model for resilience improvement strategies of microgrid systems based on parameter sensitivity is constructed. Finally, simulation verification is conducted based on the IEEE 37-node microgrid system. The results show that the proposed measurement method can scientifically and reasonably measure the resilience of the microgrid system, and the resilience improvement strategy significantly improves the operational resilience, verifying the effectiveness and robustness of the proposed analysis method.


Assuntos
Resiliência Psicológica , Reprodutibilidade dos Testes , Simulação por Computador , Sistemas Computacionais , Fontes de Energia Elétrica
17.
PLoS One ; 19(4): e0302275, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38626177

RESUMO

Although deep-learning methods can achieve human-level performance in boundary detection, their improvements mostly rely on larger models and specific datasets, leading to significant computational power consumption. As a fundamental low-level vision task, a single model with fewer parameters to achieve cross-dataset boundary detection merits further investigation. In this study, a lightweight universal boundary detection method was developed based on convolution and a transformer. The network is called a "transformer with difference convolutional network" (TDCN), which implies the introduction of a difference convolutional network rather than a pure transformer. The TDCN structure consists of three parts: convolution, transformer, and head function. First, a convolution network fused with edge operators is used to extract multiscale difference features. These pixel difference features are then fed to the hierarchical transformer as tokens. Considering the intrinsic characteristics of the boundary detection task, a new boundary-aware self-attention structure was designed in the transformer to provide inductive bias. By incorporating the proposed attention loss function, it introduces the direction of the boundary as strongly supervised information to improve the detection ability of the model. Finally, several head functions with multiscale feature inputs were trained using a bidirectional additive strategy. In the experiments, the proposed method achieved competitive performance on multiple public datasets with fewer model parameters. A single model was obtained to realize universal prediction even for different datasets without retraining, demonstrating the effectiveness of the method. The code is available at https://github.com/neulmc/TDCN.


Assuntos
Conscientização , Baixa Visão , Humanos , Fontes de Energia Elétrica , Gestão da Informação , Menopausa
18.
Artigo em Inglês | MEDLINE | ID: mdl-38573823

RESUMO

Escherichia coli were engineered to selectively adsorb and recover lithium from the environment by employing a bacterial cell surface display strategy. Lithium binding peptide (LBP1) was integrated into the Escherichia coli membrane protein OmpC. The effect of environmental conditions on the adsorption of lithium by a recombinant strain was evaluated, and lithium particles on the cellular surface were analyzed by FE-SEM and XRD. To elevate the lithium adsorption, dimeric, trimeric, and tetrameric repeats of the LBP1 peptide were constructed and displayed on the surface of E. coli. The constructed recombinant E. coli displaying the LBP1 trimer was applied to real industrial lithium battery wastewater to recover lithium.


Assuntos
Escherichia coli , Lítio , Porinas , Escherichia coli/genética , Escherichia coli/metabolismo , Adsorção , Resíduos Industriais , Proteínas da Membrana Bacteriana Externa/genética , Proteínas da Membrana Bacteriana Externa/metabolismo , Proteínas de Escherichia coli/genética , Proteínas de Escherichia coli/metabolismo , Águas Residuárias/microbiologia , Fontes de Energia Elétrica , Técnicas de Visualização da Superfície Celular , Proteínas Recombinantes/genética
19.
Waste Manag ; 181: 199-210, 2024 May 30.
Artigo em Inglês | MEDLINE | ID: mdl-38643515

RESUMO

Supercritical CO2 (scCO2) extraction assisted by complexing copolymers is a promising process to recover valuable metals from lithium-ion batteries (LIBs). CO2, in addition to being non-toxic, abundant and non-flammable, allows an easy separation of metal-complexes from the extraction medium by depressurization, limiting the wastewater production. In this study, CO2-philic gradient copolymers bearing phosphonic diacid complexing groups (poly(vinylbenzylphosphonic diacid-co-1,1,2,2-tetrahydroperfluorodecylacrylate), p(VBPDA-co-FDA)) were synthesized for the extraction of lithium and cobalt from LiCoO2 cathode material. Notably, the copolymer was able to play the triple role of leaching agent, complexing agent and surfactant. The proof of concept for leaching, complexation and extraction was achieved, using two different extraction systems. A first extraction system used aqueous hydrogen peroxide as reducing agent while it was replaced by ethanol in the second extraction system. The scCO2 extraction conditions such as extraction time, temperature, functional copolymer concentration, and the presence of additives were optimized to improve the metals extraction from LiCoO2 cathode material, leading to an extraction efficiency of Li and Co up to ca. 75 % at 60 °C and 250 bar.


Assuntos
Dióxido de Carbono , Cobalto , Lítio , Polímeros , Cobalto/química , Cobalto/isolamento & purificação , Lítio/química , Dióxido de Carbono/química , Polímeros/química , Óxidos/química , Reciclagem/métodos , Eletrodos , Fontes de Energia Elétrica
20.
PLoS One ; 19(4): e0301980, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38669276

RESUMO

This research introduces a new designing process and analysis of an innovative Silicon-on-Insulator Metal-Semiconductor Field-Effect (SOI MESFET) structure that demonstrates improved DC and RF characteristics. The design incorporates several modifications to control and reduce the electric field concentration within the channel. These modifications include relocating the transistor channel to sub-regions near the source and drain, adjusting the position of the gate electrode closer to the source, introducing an aluminum layer beneath the channel, and integrating an oxide layer adjacent to the gate. The results show that the AlOx-MESFET configuration exhibits a remarkable increase of 128% in breakdown voltage and 156% in peak power. Furthermore, due to enhanced conductivity and a significant reduction in gate-drain capacitance, there is a notable improvement of 53% in the cut-off frequency and a 28% increase in the maximum oscillation frequency. Additionally, the current gain experiences a boost of 15%. The improved breakdown voltage and peak power make it suitable for applications requiring robust performance under high voltage and power conditions. The increased maximum oscillation frequency and cut-off frequency make it ideal for high-frequency applications where fast signal processing is crucial. Moreover, the enhanced current gain ensures efficient amplification of signals. The introduced SOI MESFET structure with its modifications offers significant improvements in various performance metrics. It provides high oscillation frequency, better breakdown voltage and good cut-off frequency, and current gain compared to the traditional designs. These enhancements make it a highly desirable choice for applications that demand high-frequency and high-power capabilities.


Assuntos
Desenho de Equipamento , Silício , Silício/química , Semicondutores , Transistores Eletrônicos , Condutividade Elétrica , Fontes de Energia Elétrica , Metais/química
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