Search Results
Spatially explicit analysis identifies significant potential for bioenergy with carbon capture and storage in China
As China ramped-up coal power capacities rapidly while CO2 emissions need to decline, these capacities would turn into stranded assets. To deal with this risk, a promising option is to retrofit these capacities to co-fire with biomass and eventually upgrade to CCS operation (BECCS), but the feasibility is debated with respect to negative impacts on broader sustainability issues. Here we present a data-rich spatially explicit approach to estimate the marginal cost curve for decarbonizing the power sector in China with BECCS. We identify a potential of 222 GW of power capacities in 2836 counties generated by co-firing 0.9 Gt of biomass from the same county, with half being agricultural residues. Our spatially explicit method helps to reduce uncertainty in the economic costs and emissions of BECCS, identify the best opportunities for bioenergy and show the limitations by logistical challenges to achieve carbon neutrality in the power sector with large-scale BECCS in China.
Calculation of the steady states in dynamic semiconductor laser models
We discuss numerical challenges in calculating stable and unstable steady states of widely used dynamic semiconductor laser models. Knowledge of these states is valuable when analyzing laser dynamics and different properties of the lasing states. The example simulations and analysis mainly rely on 1(time)+1(space)-dimensional traveling-wave models, where the steady state defining conditions are formulated as a system of nonlinear algebraic equations. The performed steady state calculations reveal limitations of the Lang-Kobayashi model, explain nontrivial bias threshold relations in lasers with several electrical contacts, or predict and explain transient dynamics when simulating such lasers.
Detecting striations via the lateral photovoltage scanning method without screening effect
The lateral photovoltage scanning method (LPS) detects doping inhomogeneities in semiconductors such as Si, Ge and SixGe1−x in a cheap, fast and nondestructive manner. LPS relies on the bulk photovoltaic effect and thus can detect any physical quantity affecting the band profiles of the sample. LPS finite volume simulation using commercial software suffer from long simulation times and convergence instabilities. We present here an open-source finite volume simulation for a 2D Si sample using the ddfermi simulator. For low injection conditions we show that the LPS voltage is proportional to the doping gradient. For higher injection conditions, we directly show how the LPS voltage and the doping gradient differ and link the physical effect of lower local resolution to the screening effect. Previously, the loss of local resolution was assumed to be only connected to the enlargement of the excess charge carrier distribution.
Hybrid nanostructured particles via surfactant-free double miniemulsion polymerization
Double emulsions are complex fluid systems, in which droplets of a dispersed liquid phase contain even smaller dispersed liquid droplets. Particularly, water-in-oil-in-water double emulsions provide significant advantages over simple oil-in-water emulsions for microencapsulation, such as carrier of both aqueous and oily payloads and sustained release profile. However, double emulsions are thermodynamically unstable systems consisting typically of relatively large droplets. Here we show that nanoscale water-in-oil-in-water double emulsions can be prepared by adding a silica precursor polymer, hyperbranched polyethoxysiloxane, to the oil phase without any additional surfactants. The resulting double miniemulsions are transformed to robust water@SiO2@polymer@SiO2 nanocapsules via conversion of the precursor to silica and polymerization of the oil phase. Other intriguing nanostructures like nanorattles and Janus-like nanomushrooms can also be obtained by changing preparation conditions. This simple surfactant-free double miniemulsion polymerization technique opens a promising avenue for mass production of various complex hybrid nanostructures that are amenable to numerous applications.
Simulation and analysis of high-brightness tapered ridge-waveguide lasers
In this work, a simulation-based analysis of a CW-driven tapered ridge-waveguide laser design is presented. Measurements of these devices delivered high lateral brightness values of 4 W · mm - 1mrad - 1 at 2.5W optical output power. First, active laser simulations are performed to reproduce these results. Next, the resulting complex valued intra-cavity refractive index distributions are the basis for a modal and beam propagation analysis, which demonstrates the working principle and limitation of the underlying lateral mode filter effect. Finally, the gained understanding is the foundation for further design improvements leading to lateral brightness values of up to 10 W · mm - 1mrad - 1 predicted by simulations.
Correction to: Numerical simulation of TEM images for In(Ga)As/GaAs quantum dots with various shapes
Correction to: Optical and Quantum Electronics (2020) 52:257 https://doi.org/10.1007/s11082-020-02356-y
Data-Driven Discovery of Stochastic Differential Equations
Stochastic differential equations (SDEs) are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different sources. The identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system's dynamics. The practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data resources. This study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning (SBL) technique to search for a parsimonious, yet physically necessary representation from the space of candidate basis functions. More importantly, we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series data. The effectiveness of the proposed framework is demonstrated using real data on stock and oil prices, bearing variation, and wind speed, as well as simulated data on well-known stochastic dynamical systems, including the generalized Wiener process and Langevin equation. This framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences, economics, and engineering fields for analysis, prediction, and decision making.
Numerical simulation of TEM images for In(Ga)As/GaAs quantum dots with various shapes
We present a mathematical model and a tool chain for the numerical simulation of TEM images of semiconductor quantum dots (QDs). This includes elasticity theory to obtain the strain profile coupled with the Darwin–Howie–Whelan equations, describing the propagation of the electron wave through the sample. We perform a simulation study on indium gallium arsenide QDs with different shapes and compare the resulting TEM images to experimental ones. This tool chain can be applied to generate a database of simulated TEM images, which is a key element of a novel concept for model-based geometry reconstruction of semiconductor QDs, involving machine learning techniques.
One-shot phase-recovery using a cellphone RGB camera on a Jamin-Lebedeff microscope
Jamin-Lebedeff (JL) polarization interference microscopy is a classical method for determining the change in the optical path of transparent tissues. Whilst a differential interference contrast (DIC) microscopy interferes an image with itself shifted by half a point spread function, the shear between the object and reference image in a JL-microscope is about half the field of view. The optical path difference (OPD) between the sample and reference region (assumed to be empty) is encoded into a color by white-light interference. From a color-table, the Michel-Levy chart, the OPD can be deduced. In cytology JL-imaging can be used as a way to determine the OPD which closely corresponds to the dry mass per area of cells in a single image. Like in other interference microscopy methods (e.g. holography), we present a phase retrieval method relying on single-shot measurements only, thus allowing real-time quantitative phase measurements. This is achieved by adding several customized 3D-printed parts (e.g. rotational polarization-filter holders) and a modern cellphone with an RGB-camera to the Jamin-Lebedeff setup, thus bringing an old microscope back to life. The algorithm is calibrated using a reference image of a known phase object (e.g. optical fiber). A gradient-descent based inverse problem generates an inverse look-up-table (LUT) which is used to convert the measured RGB signal of a phase-sample into an OPD. To account for possible ambiguities in the phase-map or phase-unwrapping artifacts we introduce a total-variation based regularization. We present results from fixed and living biological samples as well as reference samples for comparison.