An agile model for the eco-design of electric vehicle Li-ion batteries

Numerous design choices need to be made at several levels when designing high-tech products: technology, processes, architecture, components, materials… and these choices need to be made in relation to the product life cycle with the corresponding experts for each stage of the life cycle. At the same time, to ensure product sustainability, a specific focus on the future potential environmental impacts is highly recommended. In this research, an agile model is proposed to help designers make decisions while monitoring environmental performance indicators of high-tech solutions. The concept of Critical Product Life Cycle Parameters had to be introduced to facilitate the eco-design of the final product. The approach is illustrated by the Electric Vehicle Li-Ion Batteries case study.     

Electrospun fiber membranes from blends of poly(vinylidene fluoride) with fouling‐resistant zwitterionic copolymers

Nonwoven super‐hydrophobic fiber membranes have potential applications in oil–water separation and membrane distillation, but fouling negatively impacts both applications. Membranes were prepared from blends comprising poly(vinylidene fluoride) (PVDF) and random zwitterionic copolymers of poly(methyl methacrylate) (PMMA) with sulfobetaine methacrylate (SBMA) or with sulfobetaine‐2‐vinylpyridine (SB2VP). PVDF imparts mechanical strength to the membrane, while the copolymers enhance fouling resistance. Blend composition was varied by controlling the PVDF‐to‐copolymer ratio. Nonwoven fiber membranes were obtained by electrospinning solutions of PVDF and the copolymers in a mixed solvent of N,N‐dimethylacetamide and acetone. The PVDF crystal phases and crystallinities of the blends were studied using wide‐angle X‐ray diffraction and differential scanning calorimetry (DSC). PVDF crystallized preferentially into its polar β‐phase, though its degree of crystallinity was reduced with increased addition of the random copolymers. Thermogravimetry (TG) showed that the degradation temperatures varied systematically with blend composition. PVDF blends with either copolymer showed significant increase of fouling resistance. Membranes prepared from blends containing 10% P(MMA‐ran‐SB2VP) had the highest fouling resistance, with a fivefold decrease in protein adsorption on the surface, compared to homopolymer PVDF. They also exhibited higher pure water flux, and better oil removal in oil–water separation experiments. © 2018 Society of Chemical Industry     

Evolution of collaboration and optimization of impact: self-organization in multinational research

Scientometrics - The expanding presence of multinational research teams highlights the importance of characterizing the outcomes of international collaboration. Herein, we characterize the...     

A low-phase-noise K-band CMOS VCO

A novel circuit topology for low-phase-noise voltage controlled oscillators (VCOs) is presented in this letter. By employing a PMOS cross-coupled pair with a capacitive feedback, superior circuit performance can be achieved especially at higher frequencies. Based on the proposed architecture, a prototype VCO implemented in a 0.18-/spl mu/m CMOS process is demonstrated for K-band applications. From the measurement results, the VCO exhibits a 510-MHz frequency tuning range at 20GHz. The output power and the phase noise at 1-MHz offset are -3dBm and -111dBc/Hz, respectively. The fabricated circuit consumes a dc power of 32mW from a 1.8-V supply voltage.     

Artificial neural networks and statistical modeling for electronic stress prediction using thermal profiling

Electronic components are constantly under stress due to factors such as signal density, temperature, humidity, and high current and voltage. Relatively little research has emphasized stress-level prediction under voltage stress. The purpose of this paper was to develop an electronic thermal profile model for stress-level prediction utilizing neural network (NN) and statistical approaches, such as multivariate regression models. Electronic components were removed from boards, subjected to different levels of stress, then replaced. An infrared camera was then used to capture information about component temperature changes over time under normal operating and stress conditions. Statistical analysis of the captured images suggests a strong correlation between thermal profiles and voltage stress levels. Artificial neural network (ANN) and statistical approaches were used to model temperature change profiles for components that had been stressed at different levels, and their predictive ability was compared. Separate data sets were used for model development and model verification. ANN prediction rates were around 70%, compared to 30% for the statistical approach. Experiments were also conducted to evaluate the robustness of the ANN model to the presence of noise in the data. Results suggested that the ANN model was able to accommodate the presence of noise. Various backpropagation (BP) learning algorithms were also evaluated and yielded similar average error rates. A 3-2-1 ANN topology performed better than 3-3-1 or 3-2-2-1 topologies, perhaps because the 3-2-1 topology has a higher data sample to nodes ratio than the other topologies.     

Fabrication of 1.55 μm VCSELs on Si using metallic bonding

Using AuGeNiCr multilayered metals as the wafer bonding medium, long-wavelength GaInAsP/InP vertical cavity surface emitting lasers employing Al-oxide/Si as the upper and lower distributed Bragg reflectors were fabricated on Si substrate with the bonding interface formed outside the vertical cavity surface emitting laser cavity. Laser emission at 1.545 μm was measured under pulsed operations near room temperature. The low-temperature metallic bonding process demonstrates a great potential in device fabrication     

Highly Efficient Orange and Green Solid‐State Light‐Emitting Electrochemical Cells Based on Cationic IrIII Complexes with Enhanced Steric Hindrance

Highly efficient orange and green emission from single‐layered solid‐state light‐emitting electrochemical cells based on cationic transition‐metal complexes [Ir(ppy)₂sb]PF₆ and [Ir(dFppy)₂sb]PF₆ (where ppy is 2‐phenylpyridine, dFppy is 2‐(2,4‐difluorophenyl)pyridine, and sb is 4,5‐diaza‐9,9′‐spirobifluorene) is reported. Photoluminescence measurements show highly retained quantum yields for [Ir(ppy)₂sb]PF₆ and [Ir(dFppy)₂ sb]PF₆ in neat films (compared with quantum yields of these complexes dispersed in m‐bis(N‐carbazolyl)benzene films). The spiroconfigured sb ligands effectively enhance the steric hindrance of the complexes and reduce the self‐quenching effect. The devices that use single‐layered neat films of [Ir(ppy)₂sb]PF₆ and [Ir(dFppy)₂sb]PF₆ achieve high peak external quantum efficiencies and power efficiencies of 7.1 % and 22.6 lm W^–1) at 2.5 V, and 7.1 % and 26.2 lm W^–1 at 2.8 V, respectively. These efficiencies are among the highest reported for solid‐state light‐emitting electrochemical cells, and indicate that cationic transition‐metal complexes containing ligands with good steric hindrance are excellent candidates for highly efficient solid‐state electrochemical cells.