871 research outputs found
Radiation resistance of Ge, Ge0.93Si0.07, GaAs and Al0.08Ga0.92 as solar cells
Solar cells made of Ge, Ge(0.93)Si(0.07) alloys, GaAs and Al(0.08)Ga(0.92)As were irradiated in two experiments with 1-meV electrons at fluences as great as 1 x 10(exp 16) cm(exp-2). Several general trends have emerged. Low-band-gap Ge and Ge(0.93)Si(0.07) cells show substantial resistance to radiation-induced damage. The two experiments showed that degradation is less for Al(0.08)Ga(0.92)As cells than for similarly irradiated GaAs cells. Compared to homojunctions, cells with graded-band-gap emitters did not show the additional resistance to damage in the second experiment that had been seen in the first. The thickness of the emitter is a key parameter to limit the degradation in GaAs devices
Social and technological dimensions; and constraint analysis in sugarcane cultivation of Theni district of Tamil Nadu, India
Bridging the yield gap in any crop cultivation should be the prime objective of any research efforts. By following the recommended production-cum-protection technologies, farmers can bridge the yield gap in any crop. As per sugarcane is concerned, the average cane yield in Tamil Nadu is 101 t/ha, which is lower than the potential yield of 203.7 t/ha resulting in yield gap of 50.42%. With this in mind, a study has been initiated to explore the social and technology dimensions and constraints involved in cane cultivation to addressing the issue of yield gap. Six blocks from Theni district under Rajshree Sugars & Chemicals Ltd were selected as study area. Information collected from sixty sugarcane farmers with semi structured interview schedule. The study revealed that majority (91.7%) of the respondents had more than 5 years of experience in sugarcane cultivation. Further, it revealed that the technologies, seed rate (83.3%), planting season (75.00%), primary tillage with mould board / disc plough (67%), gap filling, two split application of N and K (58.3%) and Organic fertilizer application (58.3%), stubble shaving, off baring (50.0%) had adoption rate of more than 50 percentage. Major constraints faced by cent per cent of the respondents were; non availability of labour and high labour cost, prolonged drought and water scarcity, low procuring cost per by sugar factory, yield reduction due to continuous cultivation of sugarcane. The novelty and importance of the study is that it mainly analysis all the sugarcane production and protection technologies from seed rate to harvest in three point continuum viz., fully adopted, partially adopted and not adopted
Graded-bandgap AlGaAs solar cells for AlGaAs/Ge cascade cells
Some p/n graded-bandgap Al(x)Ga(1-x)As solar cells were fabricated and show AMO conversion efficiencies in excess of 15 percent without antireflection (AR) coatings. The emitters of these cells are graded between 0.008 is less than or equal to x is less than or equal to 0.02 during growth of 0.25 to 0.30 micron thick layers. The keys to achieving this performance were careful selection of organometallic sources and scrubbing oxygen and water vapor from the AsH3 source. Source selection and growth were optimized using time-resolved photoluminescence. Preliminary radiation-resistance measurements show AlGaAs cells degraded less than GaAs cells at high 1 MeV electron fluences, and AlGaAs cells grown on GaAs and Ge substrates degrade comparably
Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Dendrite Suppression with Li Metal Anode
Next generation batteries based on lithium (Li) metal anodes have been
plagued by the dendritic electrodeposition of Li metal on the anode during
cycling, resulting in short circuit and capacity loss. Suppression of dendritic
growth through the use of solid electrolytes has emerged as one of the most
promising strategies for enabling the use of Li metal anodes. We perform a
computational screening of over 12,000 inorganic solids based on their ability
to suppress dendrite initiation in contact with Li metal anode. Properties for
mechanically isotropic and anisotropic interfaces that can be used in stability
criteria for determining the propensity of dendrite initiation are usually
obtained from computationally expensive first-principles methods. In order to
obtain a large dataset for screening, we use machine learning models to predict
the mechanical properties of several new solid electrolytes. We train a
convolutional neural network on the shear and bulk moduli purely on structural
features of the material. We use AdaBoost, Lasso and Bayesian ridge regression
to train the elastic constants, where the choice of the model depended on the
size of the training data and the noise that it can handle. Our models give us
direct interpretability by revealing the dominant structural features affecting
the elastic constants. The stiffness is found to increase with a decrease in
volume per atom, increase in minimum anion-anion separation, and increase in
sublattice (all but Li) packing fraction. Cross-validation/test performance
suggests our models generalize well. We predict over 20 mechanically
anisotropic interfaces between Li metal and 6 solid electrolytes which can be
used to suppress dendrite growth. Our screened candidates are generally soft
and highly anisotropic, and present opportunities for simultaneously obtaining
dendrite suppression and high ionic conductivity in solid electrolytes.Comment: 34 pages, 4 Figures, 3 Table, 7 pages of Supporting Informatio
A Graph theory algorithmic approach to data clustering and its Application
Clustering is the unproven classification of data items, into groups known as clusters. The clustering problem has been discussed in many area of research in many disciplines; this reflects its huge usefulness in the field of data analysis. However, clustering may be a difficult problem statistically, and the differences in assumptions in different disciplines made concepts and methodologies slow to occur. This paperpresentstaxonomy of clustering techniques, and recent advances in graphtheorytic approach. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval
Behavioural activation written self-help to improve mood, wellbeing and quality of life in people with dementia supported by informal carers (PROMOTE): study protocol for a single-arm feasibility study.
Background: Increases in life expectancy have resulted in a global rise in dementia
prevalence. Dementia is associated with poor wellbeing, low quality of life and
increased incidence of mental health difficulties such as, low mood or depression.
However, currently there is limited access to evidence-based psychological
interventions for people with dementia experiencing low mood and poor wellbeing.
Behavioural activation-based self-help, supported by informal carers and guided by
mental health professionals, may represent an effective and acceptable solution.
Methods/design: The present study is a Phase II (feasibility) single-arm trial informed
by the MRC Complex Interventions Research Methods Framework. Up to fifty
dementia participant/informal carer dyads will be recruited from a variety of settings
including primary care, dementia-specific health settings, and community outreach.
People living with dementia will receive behavioural activation based self-help and be
supported by their informal carer who has received training in the skills required to
support the self-help approach. In turn, during the use of the intervention the informal
carer will be guided by mental health professionals to help them work through the
materials and problem solve any difficulties. Consistent with the objectives of feasibility
studies, outcomes relating to recruitment from different settings, employment of
different recruitment methods, attrition, data collection procedures, clinical delivery and
acceptability of the intervention will be examined. Clinical outcomes for people with
dementia (symptoms of depression and quality of life) and informal carers (symptoms
of depression and anxiety, carer burden and quality of life) will be measured pretreatment
and at 3 months post-treatment allocation.
Discussion: This study will examine the feasibility and acceptability of a novel
behavioural activation-based self-help intervention designed to promote wellbeing and
improve low mood in people living with dementia, alongside methodological and
procedural uncertainties associated with research-related procedures. As determined
by pre-specified progression criteria, if research procedures and the new intervention
demonstrate feasibility and acceptability, results will then be used to inform the design
of a pilot randomised controlled trial (RCT) to specifically examine remaining
methodological uncertainties associated with recruitment into a randomised controlled
design.This study is collaboratively funded by Cornwall Foundation Partnership Trust, South West
Peninsula Academic Health Sciences Network and the University of Exeter
Lorenz function of BiTe/SbTe superlattices
Combining first principles density functional theory and semi-classical
Boltzmann transport, the anisotropic Lorenz function was studied for
thermoelectric BiTe/SbTe superlattices and their bulk
constituents. It was found that already for the bulk materials BiTe
and SbTe, the Lorenz function is not a pellucid function on charge
carrier concentration and temperature. For electron-doped
BiTe/SbTe superlattices large oscillatory deviations
for the Lorenz function from the metallic limit were found even at high charge
carrier concentrations. The latter can be referred to quantum well effects,
which occur at distinct superlattice periods
An efficient algorithm to calculate intrinsic thermoelectric parameters based on Landauer approach
The Landauer approach provides a conceptually simple way to calculate the
intrinsic thermoelectric (TE) parameters of materials from the ballistic to the
diffusive transport regime. This method relies on the calculation of the number
of propagating modes and the scattering rate for each mode. The modes are
calculated from the energy dispersion (E(k)) of the materials which require
heavy computation and often supply energy relation on sparse momentum (k)
grids. Here an efficient method to calculate the distribution of modes (DOM)
from a given E(k) relationship is presented. The main features of this
algorithm are, (i) its ability to work on sparse dispersion data, and (ii)
creation of an energy grid for the DOM that is almost independent of the
dispersion data therefore allowing for efficient and fast calculation of TE
parameters. The inclusion of scattering effects is also straight forward. The
effect of k-grid sparsity on the compute time for DOM and on the sensitivity of
the calculated TE results are provided. The algorithm calculates the TE
parameters within 5% accuracy when the K-grid sparsity is increased up to 60%
for all the dimensions (3D, 2D and 1D). The time taken for the DOM calculation
is strongly influenced by the transverse K density (K perpendicular to
transport direction) but is almost independent of the transport K density
(along the transport direction). The DOM and TE results from the algorithm are
bench-marked with, (i) analytical calculations for parabolic bands, and (ii)
realistic electronic and phonon results for .Comment: 16 Figures, 3 Tables, submitted to Journal of Computational
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