Effect of Different Hot-Pressing Pressure and Temperature on the Performance of Titanium Mesh-Based MEA for DMFC.

The hot-pressing process of the membrane electrode assembly (MEA) is one of the research hotspots in the field of the fuel cell. To obtain suitable titanium mesh-based MEA hot pressing process parameters, titanium mesh was used as electrode substrate material. The anode and cathode of MEA were prepared by the drip-coated method, and the titanium mesh-based MEA was prepared under different hot-pressing pressure and temperature, respectively. The performance of titanium mesh-based MEA was studied by morphological observation, elemental analysis, thickness measurement, single cell test and numerical fitting analysis. The results demonstrated that: with increasing hot-pressing pressure from 0 MPa to 10 MPa, the forming thickness of titanium mesh-based MEA is getting thin gradually, and the peak power density of titanium mesh-based MEA first increased and then gradually decreased; with increasing hot-pressing temperature from 115 °C to 155 °C, the peak power density of titanium mesh-based MEA enhanced at the beginning and then also gradually decreased. Under the premise of a hot-pressing time of 180 s and the optimal operating temperature of DMFC of 60 °C, the appropriate hot-pressing process conditions of titanium mesh-based MEA are a hot-pressing pressure of 5 MPa and a hot-pressing temperature of 135 °C. The results can provide a technological reference for the preparation of titanium mesh MEA for DMFC.

Multi-Classification of Complex Microseismic Waveforms Using Convolutional Neural Network: A Case Study in Tunnel Engineering.

Due to the complexity of the various waveforms of microseismic data, there are high requirements on the automatic multi-classification of such data; an accurate classification is conducive for further signal processing and stability analysis of surrounding rock masses. In this study, a microseismic multi-classification (MMC) model is proposed based on the short time Fourier transform (STFT) technology and convolutional neural network (CNN). The real and imaginary parts of the coefficients of microseismic data are inputted to the proposed model to generate three classes of targets. Compared with existing methods, the MMC has an optimal performance in multi-classification of microseismic data in terms of Precision, Recall, and F1-score, even when the waveform of a microseismic signal is similar to that of some special noise. Moreover, semisynthetic data constructed by clean microseismic data and noise are used to prove the low sensitivity of the MMC to noise. Microseismic data recorded under different geological conditions are also tested to prove the generality of the model, and a microseismic signal with Mw ≥ 0.2 can be detected with a high accuracy. The proposed method has great potential to be extended to the study of exploration seismology and earthquakes.

Predictive modeling of battery degradation and greenhouse gas emissions from U.S. state-level electric vehicle operation.

Electric vehicles (EVs) are widely promoted as clean alternatives to conventional vehicles for reducing greenhouse gas (GHG) emissions from ground transportation. However, the battery undergoes a sophisticated degradation process during EV operations and its effects on EV energy consumption and GHG emissions are unknown. Here we show on a typical 24 kWh lithium-manganese-oxide-graphite battery pack that the degradation of EV battery can be mathematically modeled to predict battery life and to study its effects on energy consumption and GHG emissions from EV operations. We found that under US state-level average driving conditions, the battery life is ranging between 5.2 years in Florida and 13.3 years in Alaska under 30% battery degradation limit. The battery degradation will cause a 11.5-16.2% increase in energy consumption and GHG emissions per km driven at 30% capacity loss. This study provides a robust analytical approach and results for supporting policy making in prioritizing EV deployment in the U.S.

An investigation of the PM2.5 and NO2 concentrations and their human health impacts in the metro subway system of Suzhou, China.

This study measured the particle concentrations with an aerodynamic diameter smaller than 2.5 µm (PM2.5), nitrogen dioxide (NO2), and relative humidity (RH) at five metro subway stations in Suzhou's subway system (Lines 1 and 2). The real-time monitoring campaign was conducted from March 30th to April 10th and August 4th to August 21st, 2015. The monitoring practice was carried out during rush (7:00-9:00 AM and 17:00-19:00 PM) and regular hours (other times) at the ground and underground levels under different weather conditions with a purpose of obtaining representative data. The monitored results show that the concentrations of PM2.5 in the train carriages were lower than the concentrations at the underground platforms during both spring and summer. The mean PM2.5 concentrations at all the underground platforms in all the sub-stations monitored were significantly higher than those at the ground level. The human health impact was calculated to be 6300 annual DALYs (or 375 deaths) due to exposure to the subway system in Suzhou according to the UNEP-SETAC toxicity (USEtox) model. Linear regression models were applied to evaluate the relationships between the PM2.5, NO2 concentrations, and RH. We found that a 10% increment in RH from the current average level of 50-60% can lead to a 9.8 µg m-3 concentration decrease in PM2.5. This further results in the total human health impact being reduced to 2451 DALYs (150-4753 DALYs), representing a 20% decrease (1.2-38%).

Incorporating denitrification-decomposition method to estimate field emissions for Life Cycle Assessment.

This study focuses on a detailed Life Cycle Assessment (LCA) for flax cultivation in Northern France. Nitrogen related field emissions are derived both from a process-oriented DeNitrification-DeComposition (DNDC) method and the generic Intergovernmental Panel on Climate Change (IPCC) method. Since the IPCC method is synthesised from field measurements at sites with various soil types, climate conditions, and crops, it contains significant uncertainties. In contrast, the outputs from the DNDC method are considered as more site specific as it is built according to complex models of soil science. As it is demonstrated in this paper the emission factors from the DNDC method and the recommended values from the IPCC method exhibit significant variations for the case of flax cultivation. The DNDC based emission factor for direct N2O emission, which is a strong greenhouse gas, is 0.25-0.5%, significantly lower than the recommend 1% level derived from the IPCC method. The DNDC method leads to a reduction of 17% in the impact category of climate change per kg retted flax straw production from the level obtained from the IPCC method. Much higher reductions are recorded for particulate matter formation, terrestrial acidification, and marine eutrophication impact categories. Meanwhile, based on the DNDC and IPCC methods, a comparative LCA per kg flax straw is presented. For both methods sensitivity analysis as well as comparison of uncertainties parameterisation of the N2O estimates via Monte-Carlo analysis are performed. The DNDC method incorporates more relevant field emissions from the agricultural life cycle phase, which can also improve the quality of the Life Cycle Inventory as well as allow more precise uncertainty calibration in the LCA inventory.

Band gap opening and semiconductor-metal phase transition in (n, n) single-walled carbon nanotubes with distinctive boron-nitrogen line defect.

Band gap opening and modulating are critical in dictating the functionalities of single walled carbon nanotubes (SWCNTs) in a broad array of nano-devices. Using first-principles density functional theory calculations, a class of semiconducting armchair SWCNTs with a distinctive BN line defect are studied, showing a super capacity to open the band gap of (4, 4) SWCNT to as large as 0.86 eV, while the opened band gap are found decreasing with the increasing diameters of SWCNTs. The opened band gap of SWCNTs can also be successfully modulated through both mechanical and electrical approaches by applying compressive uniaxial strain and electric field. This study provides novel insights into the large band gap opening and modulating of SWCNTs and could be useful in facilitating future applications of SWCNTs in electronic, optical and thermoelectric devices.

Life Cycle Assessment of Titania Perovskite Solar Cell Technology for Sustainable Design and Manufacturing.

Perovskite solar cells have attracted enormous attention in recent years due to their low cost and superior technical performance. However, the use of toxic metals, such as lead, in the perovskite dye and toxic chemicals in perovskite solar cell manufacturing causes grave concerns for its environmental performance. To understand and facilitate the sustainable development of perovskite solar cell technology from its design to manufacturing, a comprehensive environmental impact assessment has been conducted on titanium dioxide nanotube based perovskite solar cells by using an attributional life cycle assessment approach, from cradle to gate, with manufacturing data from our laboratory-scale experiments and upstream data collected from professional databases and the literature. The results indicate that the perovskite dye is the primary source of environmental impact, associated with 64.77% total embodied energy and 31.38% embodied materials consumption, contributing to more than 50% of the life cycle impact in almost all impact categories, although lead used in the perovskite dye only contributes to about 1.14% of the human toxicity potential. A comparison of perovskite solar cells with commercial silicon and cadmium-tellurium solar cells reveals that perovskite solar cells could be a promising alternative technology for future large-scale industrial applications.

Spark Plasma Sintering As a Solid-State Recycling Technique: The Case of Aluminum Alloy Scrap Consolidation.

Recently, "meltless" recycling techniques have been presented for the light metals category, targeting both energy and material savings by bypassing the final recycling step of remelting. In this context, the use of spark plasma sintering (SPS) is proposed in this paper as a novel solid-state recycling technique. The objective is two-fold: (I) to prove the technical feasibility of this approach; and (II) to characterize the recycled samples. Aluminum (Al) alloy scrap was selected to demonstrate the SPS effectiveness in producing fully-dense samples. For this purpose, Al alloy scrap in the form of machining chips was cold pre-compacted and sintered bellow the solidus temperature at 490 °C, under elevated pressure of 200 MPa. The dynamic scrap compaction, combined with electric current-based joule heating, achieved partial fracture of the stable surface oxides, desorption of the entrapped gases and activated the metallic surfaces, resulting in efficient solid-state chip welding eliminating residual porosity. The microhardness, the texture, the mechanical properties, the microstructure and the density of the recycled specimens have been investigated. An X-ray computed tomography (CT) analysis confirmed the density measurements, revealing a void-less bulk material with homogeneously distributed intermetallic compounds and oxides. The oxide content of the chips incorporated within the recycled material slightly increases its elastic properties. Finally, a thermal distribution simulation of the process in different segments illustrates the improved energy efficiency of this approach.