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.     

Nonlinear protein binding and enzyme heterogeneity: effects on hepatic drug removal

The kinetics of substrate removal by the liver and the resulting nonlinear changes in unbound fraction along the flow path at varying input drug concentrations were examined by a model simulation study. Specifically, we varied the binding association constant, KA, and the Michaelis-Menten constants (Km and Vmax) to examine the steady state drug removal (expressed as hepatic extraction ratio E) and changes in drug binding for (i) unienzyme systems and (ii) simple, parallel metabolic pathways; zonal metabolic heterogeneity was also added as a variable. At low KA, E declined with increasing input drug concentration, due primarily to saturation of enzymes; only small differences in binding were present across the liver. At high KA, a parabolic profile for E with concentration was observed; changes in unbound fraction between the inlet and the outlet of the liver followed in parallel fashion. Protein binding was the rate-determining step at low input drug concentrations, whereas enzyme saturation was the rate-controlling factor at high input drug concentration. Heterogeneous enzymic distribution modulated changes in unbound fraction within the liver and at the outlet. Despite marked changes in unbound fraction occurring within the liver for different enzymic distributions, the overall transhepatic differences were relatively small. We then investigated the logarithmic average unbound concentration and the length averaged concentration as estimates of substrate concentration in liver in the presence of nonlinear drug binding. Fitting of simulated data, with and without assigned random error (10%), to the Michaelis-Menten equation was performed; fitting was repeated for simulated data obtained with presence of a specific inhibitor of the high-affinity, anteriorly distributed pathway. Results were similar for both concentration terms: accurate estimates were obtained for anterior, high affinity pathways; an overestimation of parameters was observed for the lower affinity posteriorly distributed pathways. Improved estimations were found for posteriorly distributed pathways upon inhibition with specific inhibitors; with added random error, however, the improvement was much decreased. We applied the method for fitting of several sets of metabolic data obtained from rat liver perfusion studies performed with salicylamide (SAM) (i) without and (ii) with the presence of 2,6-dichloro-4-nitrophenol (DCNP), a SAM sulfation inhibitor. The fitted results showed that SAM sulfation was a high-affinity high-capacity pathway; SAM glucuronidation was of lower affinity but comparable capacity as the sulfation pathway, whereas SAM hydroxylation was of lower affinity and lower capacity.     

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.