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A low-bandgap dimeric porphyrin molecule for 10% efficiency solar cells with small photon energy loss
Dimeric porphyrin molecules have great potential as donor materials for high performance bulk heterojunction organic solar cells (OSCs). Recently reported dimeric porphyrins bridged by ethynylenes showed power conversion efficiencies (PCEs) of more than 8%. In this study, we design and synthesize a new conjugated dimeric D-A porphyrin ZnP2BT-RH, in which the two porphyrin units are linked by an electron accepting benzothiadiazole (BT) unit. The introduction of the BT unit enhances the electron delocalization, resulting in a lower highest occupied molecular orbital (HOMO) energy level and an increased molar extinction coefficient in the near-infrared (NIR) region. The bulk heterojunction solar cells with ZnP2BT-RH as the donor material exhibit a high PCE of up to 10% with a low energy loss (Eloss) of only 0.56 eV. The 10% PCE is the highest for porphyrin-based OSCs with a conventional structure, and this Eloss is also the smallest among those reported for small molecule-based OSCs with a PCE higher than 10% to date
Local spin polarisation of electrons in Rashba semiconductor nanowires: effects of the bound state
The local spin polarisation (LSP) of electrons in two typical semiconductor
nanowires under the modulation of Rashba spin-orbit interaction (SOI) is
investigated theoretically. The influence of both the SOI- and
structure-induced bound states on the LSP is taken into account via the
spin-resolved lattice Green function method. It is discovered that high
spin-density islands with alternative signs of polarisation are formed inside
the nanowires due to the interaction between the bound states and the Rashba
effective magnetic field. Further study shows that the spin-density islands
caused by the structure-induced bound state exhibit a strong robustness against
disorder. These findings may provide an efficient way to create local magnetic
moments and store information in semiconductors.Comment: 8 pages, 3 figure
Perturbational approach to the quantum capacity of additive Gaussian quantum channel
For a quantum channel with additive Gaussian quantum noise, at the large
input energy side, we prove that the one shot capacity is achieved by the
thermal noise state for all Gaussian state inputs, it is also true for
non-Gaussian input in the sense of first order perturbation. For a general case
of copies input, we show that up to first order perturbation, any
non-Gaussian perturbation to the product thermal state input has a less quantum
information transmission rate when the input energy tend to infinitive.Comment: 5 page
Perturbation theory of von Neumann Entropy
In quantum information theory, von Neumann entropy plays an important role.
The entropies can be obtained analytically only for a few states. In continuous
variable system, even evaluating entropy numerically is not an easy task since
the dimension is infinite. We develop the perturbation theory systematically
for calculating von Neumann entropy of non-degenerate systems as well as
degenerate systems. The result turns out to be a practical way of the expansion
calculation of von Neumann entropy.Comment: 7 page
The impact of groundwater drawdown and vacuum pressure on sinkhole development. Physical laboratory models
A considerable proportion of the damaging sinkholes worldwide correspond to human-induced subsidence events related to groundwater withdrawal and the associated water-table decline (e.g. aquifer overexploitation, dewatering for mining). Buoyancy loss in pre-existing cavity roofs is generally claimed to be the main underlying physical mechanism. It has been also postulated that rapid water-table drawdowns may create a vacuum effect in the subsurface and contribute to enhance sinkhole activity in karstic terrains with a low effective porosity cover. Our laboratory physical model explores the role played by vacuum pressure induced water-table drops with different magnitudes and rates on sinkhole development, simulating an invariable mantled karst comprising cavernous bedrock and a low-permeability cover. The multiple tests performed include real-time monitoring of the water level drawdown (magnitude, duration, rate), the negative air pressures in the bedrock cavity and the cover, and several features of the subsidence phenomena (deformation style, size, magnitude, rate). The main findings derived from the test results include: (1) Vacuum pressure may trigger the development of cover collapse sinkholes in areas with low-permeability covers. (2) Different water-table decline patterns (magnitude, duration, rate) may result in different subsidence styles or rheological behaviours: sagging versus collapse. (3) Ground fissuring, frequently related to extension at the margin of sagging depressions, may cancel or significantly diminish the vacuum effect. (4) An overall direct relationship between the water-table decline rate and the subsidence rate. Some possible strategies are proposed to ameliorate the adverse effect of the negative air pressure on sinkhole hazard, which most probably has a local impact restricted by the concurrence of rapid water drawdowns and low-permeability covers
Data discovery of low dimensional fluid dynamics of turbulent flows
Discovering governing equations from data, in particular high dimensional
data, is challenging in various fields of science and engineering, and it has
potential to revolutionise the science and technology in this big data era.
This paper combines sparse identification and deep learning with non-linear
fluid dynamics, in particular the turbulent flows, to discover governing
equations of nonlinear fluid dynamics in the lower nonlinear manifold space.
The autoencoder deep neural network is used to project the high dimensional
space into a lower dimensional nonlinear manifold space. The Proper Orthogonal
Decomposition (POD) is then used to stabilise the nonlinear manifold space in
order to guarantee a stable manifold space for pattern or equations discovery
for the highly nonlinear problems such as turbulent flows. Sparse regression is
then used to discover the lower dimensional governing equations of fluid
dynamics in the lower dimensional nonlinear manifold space. What distinguishes
this approach is its ability to discover a lower dimensional governing
equations of fluid dynamics in the nonlinear manifold space. We demonstrate
this method on a number of high-dimensional fluid dynamic systems such as lock
exchange, flow past one and two cylinders. The results demonstrate that the
resulting method is capable of discovering lower dimensional governing
equations that took researchers in this community many decades years to
resolve. In addition, this model discovers dynamics in a lower dimensional
manifold space, thus leading to great computational efficiency, model
complexity and avoiding overfitting. It also provides a new insight for our
understanding of sciences such as turbulent flows
Interrogation of spline surfaces with application to isogeometric design and analysis of lattice-skin structures
A novel surface interrogation technique is proposed to compute the
intersection of curves with spline surfaces in isogeometric analysis. The
intersection points are determined in one-shot without resorting to a
Newton-Raphson iteration or successive refinement. Surface-curve intersection
is required in a wide range of applications, including contact, immersed
boundary methods and lattice-skin structures, and requires usually the solution
of a system of nonlinear equations. It is assumed that the surface is given in
form of a spline, such as a NURBS, T-spline or Catmull-Clark subdivision
surface, and is convertible into a collection of B\'ezier patches. First, a
hierarchical bounding volume tree is used to efficiently identify the B\'ezier
patches with a convex-hull intersecting the convex-hull of a given curve
segment. For ease of implementation convex-hulls are approximated with k-dops
(discrete orientation polytopes). Subsequently, the intersections of the
identified B\'ezier patches with the curve segment are determined with a
matrix-based implicit representation leading to the computation of a sequence
of small singular value decompositions (SVDs). As an application of the
developed interrogation technique the isogeometric design and analysis of
lattice-skin structures is investigated. The skin is a spline surface that is
usually created in a computer-aided design (CAD) system and the periodic
lattice to be fitted consists of unit cells, each containing a small number of
struts. The lattice-skin structure is generated by projecting selected lattice
nodes onto the surface after determining the intersection of unit cell edges
with the surface. For mechanical analysis, the skin is modelled as a
Kirchhoff-Love thin-shell and the lattice as a pin-jointed truss. The two types
of structures are coupled with a standard Lagrange multiplier approach
Topologically robust CAD model generation for structural optimisation
Computer-aided design (CAD) models play a crucial role in the design,
manufacturing and maintenance of products. Therefore, the mesh-based finite
element descriptions common in structural optimisation must be first translated
into CAD models. Currently, this can at best be performed semi-manually. We
propose a fully automated and topologically accurate approach to synthesise a
structurally-sound parametric CAD model from topology optimised finite element
models. Our solution is to first convert the topology optimised structure into
a spatial frame structure and then to regenerate it in a CAD system using
standard constructive solid geometry (CSG) operations. The obtained parametric
CAD models are compact, that is, have as few as possible geometric parameters,
which makes them ideal for editing and further processing within a CAD system.
The critical task of converting the topology optimised structure into an
optimal spatial frame structure is accomplished in several steps. We first
generate from the topology optimised voxel model a one-voxel-wide voxel chain
model using a topology-preserving skeletonisation algorithm from digital
topology. The weighted undirected graph defined by the voxel chain model yields
a spatial frame structure after processing it with standard graph algorithms.
Subsequently, we optimise the cross-sections and layout of the frame members to
recover its optimality, which may have been compromised during the conversion
process. At last, we generate the obtained frame structure in a CAD system by
repeatedly combining primitive solids, like cylinders and spheres, using
boolean operations. The resulting solid model is a boundary representation
(B-Rep) consisting of trimmed non-uniform rational B-spline (NURBS) curves and
surfaces
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