Carbon Ion Implanted Silicon for Schottky Light-Emitting Diodes

Research in the field of Photonics is in part, directed at the application of light-emitting materials based on silicon platforms. In this work silicon wafers are modified by carbon ion implantation to incorporate silicon carbide, a known light-emitting material. Ion beam synthesis treatments are applied with implant energy of 20 keV, and ion fluences of 3, 5 and 10 × 1016 ions/cm2 at both ambient temperature and high temperature (400 °C). The samples are annealed at 1000 °C, after implantation. The carbon ion implanted silicon is characterized using Raman and Fourier transform infrared spectroscopic techniques, grazing-incidence X-ray diffraction, transmission electron microscopy and electron energy loss spectroscopy. The materials are observed to have a multilayer structure, where the ambient temperature implanted materials have an amorphous silicon layer, and an amorphous silicon layer with carbon-rich, nanoscale inclusions. The high temperature implanted materials have the same layers, with an additional polycrystalline Si layer at the interface between the implanted layer and the target substrate and the amorphous Si layer with SiC inclusions is reduced in thickness compared to the ambient temperature samples. The carbon-rich inclusions are confirmed to be SiC, with no evidence of carbon clusters in the materials observed using Raman spectroscopy. The carbon ion-implanted material is used to fabricate Schottky diodes having a semitransparent gold contact at the implanted surface, and an aluminum contact on the opposite side. The diodes are tested using current-voltage measurements between -12 and +15 V. No reverse breakdown is observed for any of the diodes. The turn-on voltages for the ambient temperature implanted samples are 2.6±0.1 V, 2.8±0.6 V and 3.9±0.1 V for the 3, 5 and 10 × 1016 ions/cm2 samples, respectively. For the high temperature implanted samples, the turn-on voltages are 3.2±0.1 V, 2.7±0.1 V, and 2.9±0.4 V for the implanted samples with same fluences. The diode curves are modeled using the Shockley equation, and estimates are made of the ideality factor of the diodes. These are 188±16, 224.5±5.8, and 185.4±9.2 for the ambient temperature samples, and 163.6±6.3, 124.3±5.3, and 333±12 for the high temperature samples. The high ideality factor is associated with the native oxide layer on the silicon substrate and with the non-uniform, defect-rich implanted region of the carbon ion implanted silicon. Red-orange visible light emission from the diodes is observed with voltage greater than the turn-on voltage applied across the diodes. The luminescence for the ambient temperature samples is attributed to porous silicon, and amorphous silicon. The high temperature implanted samples show luminescence associated with porous silicon, nanocrystalline silicon carbide, and defects in silicon related to ion implantation. The luminescent intensity observed for the ambient temperature samples is higher than for the high temperature samples. The dominant luminescence feature in the carbon ion-implanted silicon material is porous silicon, which is described by quantum confinement of excitons in silicon

The Feasibility of a Multimodal Communication Treatment for Aphasia during Inpatient Rehabilitation

The purpose of the current study was to explore the feasibility of a multimodal communication training program implemented with people with aphasia during acute stroke rehabilitation. The purpose of the program was to improve production of alternate communication modalities (gesturing, drawing) as well as verbalization, and to facilitate switching among these modalities to resolve communication breakdowns. Two people with aphasia completed the intervention and demonstrated increased accuracy in the production of various alternate communication modalities. However, improvements in the ability to switch to an alternate modality were noted for only one participant. Clinical implications and future research directions are discussed

Calculating hospital length of stay using the Hospital Episode Statistics; a comparison of methodologies

Abstract Background Accurate calculation of hospital length of stay (LOS) from the English Hospital Episode Statistics (HES) is important for a wide range of audit and research purposes. The two methodologies which are commonly used to achieve this differ in their accuracy and complexity. We compare these methods and make recommendations on when each is most appropriate. Methods We calculated LOS using continuous inpatient spells (CIPS), which link care spanning across multiple hospitals, and spells, which do not, for six conditions with short (dyspepsia or other stomach function, ENT infection), medium (dehydration and gastroenteritis, perforated or bleeding ulcer), and long (stroke, fractured proximal femur) average LOS. We examined how inter-area comparisons (i.e. benchmarking) and temporal trends differed. We defined a classification system for spells and explored the causes of differences. Results Stroke LOS was 16.5 days using CIPS but 24% (95% CI: 23, 24) lower, at 12.6 days, using spells. Smaller differences existed for shorter-LOS conditions including dehydration and gastroenteritis (4.5 vs. 4.2 days) and ENT infection (0.9 vs. 0.8 days). Typical patient pathways differed markedly between areas and have evolved over time. One area had the third shortest stroke LOS (out of 151) using spells but the fourth longest using CIPS. These issues were most profound for stroke and fractured proximal femur, as patients were frequently transferred to a separate hospital for rehabilitation, however important disparities also existed for conditions with simpler secondary care pathways (e.g. ENT infections, dehydration and gastroenteritis). Conclusions Spell-based LOS is widely used by researchers and national reporting organisations, including the Health and Social Care Information Centre, however it can substantially underestimate the time patients spend in hospital. A widespread shift to a CIPS methodology is required to improve the quality of LOS estimates and the robustness of research and benchmarking findings. This is vital when investigating clinical areas with typically long, complex patient pathways. Researchers should ensure that their LOS calculation methodology is fully described and explicitly acknowledge weaknesses when appropriate

Using geographic variation in unplanned ambulatory care sensitive condition admission rates to identify commissioning priorities:an analysis of routine data from England

Objectives To use geographic variation in unplanned ambulatory care sensitive condition admission rates to identify the clinical areas and patient subgroups where there is greatest potential to prevent admissions and improve the quality and efficiency of care. Methods We used English Hospital Episode Statistics data from 2011/2012 to describe the characteristics of patients admitted for ambulatory care sensitive condition care and estimated geographic variation in unplanned admission rates. We contrasted geographic variation across admissions with different lengths of stay which we used as a proxy for clinical severity. We estimated the number of bed days that could be saved under several scenarios. Results There were 1.8 million ambulatory care sensitive condition admissions during 2011/2012. Substantial geographic variation in ambulatory care sensitive condition admission rates was commonplace but mental health care and short-stay (&lt;2 days) admissions were particularly variable. Reducing rates in the highest use areas could lead to savings of between 0.4 and 2.8 million bed days annually. Conclusions Widespread geographic variations in admission rates for conditions where admission is potentially avoidable should concern commissioners and could be symptomatic of inefficient care. Further work to explore the causes of these differences is required and should focus on mental health and short-stay admissions. </jats:sec

How do population, general practice and hospital factors influence ambulatory care sensitive admissions:a cross sectional study

Abstract Background Reducing unplanned hospital admissions is a key priority within the UK and other healthcare systems, however it remains uncertain how this can be achieved. This paper explores the relationship between unplanned ambulatory care sensitive condition (ACSC) admission rates and population, general practice and hospital characteristics. Additionally, we investigated if these factors had a differential impact across 28 conditions. Methods We used the English Hospital Episode Statistics to calculate the number of unplanned ACSC hospital admissions for 28 conditions at 8,029 general practices during 2011/12. We used multilevel negative binomial regression to estimate the influence of population (deprivation), general practice (size, access, continuity, quality, A&E proximity) and hospital (bed availability, % day cases) characteristics on unplanned admission rates after adjusting for age, sex and chronic disease prevalence. Results Practices in deprived areas (at the 90th centile) had 16% (95% confidence interval: 14 to 18) higher admission rates than those in affluent areas (10th centile). Practices with poorer care continuity (9%; 8 to 11), located closest to A&E (8%; 6 to 9), situated in areas with high inpatient bed availability (14%; 10 to 18) or in areas with a larger proportion of day case admissions (17%; 12 to 21) had more admissions. There were smaller associations for primary care access, clinical quality, and practice size. The strength of associations varied by ACSC. For example, deprivation was most strongly associated with alcohol related diseases and COPD admission rates, while continuity of primary care was most strongly associated with admission rates for chronic diseases such as hypertension and iron-deficiency anaemia. Conclusions The drivers of unplanned ACSC admission rates are complex and include population, practice and hospital factors. The importance of these varies markedly across conditions suggesting that multifaceted interventions are required to avoid hospital admissions and reduce costs. Several of the most important drivers of admissions are largely beyond the control of GPs. However, strategies to improve primary care continuity and avoid unnecessary short-stay admissions could lead to improved efficiency

Intensive Multimodal Communication Intervention for People with Chronic Aphasia

The purpose of this study was to examine an intensive multimodal intervention for chronic aphasia. The intervention aimed to increase successful initial use of nonverbal communication modalities to prevent communication breakdowns and to improve switching among communication modalities to repair communication breakdowns. Two people with chronic aphasia completed 10 three-hour intervention sessions across a two-week period. Participant one demonstrated increased successful initial nonverbal modality use across three words lists and increased switching to repair breakdowns. Participant two showed limited success using nonverbal modalities initially or as a repair attempt. Clinical implications and future research directions will be discussed

Keep it Simple? Predicting Primary Health Care Costs with Measures of Morbidity and Multimorbidity

In this paper we investigate the relationship between patients’ primary care costs (consultations, tests, drugs) and their age, gender, deprivation and alternative measures of their morbidity and multimorbidity. Such information is required in order to set capitation fees or budgets for general practices to cover their expenditure on providing primary care services. It is also useful to examine whether practices’ expenditure decisions vary equitably with patient characteristics. Electronic practice record keeping systems mean that there is very rich information on patient diagnoses. But the diagnostic information (with over 9000 possible diagnoses) is too detailed to be practicable for setting capitation fees or practice budgets. Some method of summarizing such information into more manageable measures of morbidity is required. We therefore compared the ability of eight measures of patient morbidity and multimorbidity to predict future primary care costs using data on 86,100 individuals in 174 English practices. The measures were derived from four morbidity descriptive systems (17 chronic diseases in the Quality and Outcomes Framework (QOF), 17 chronic diseases in the Charlson scheme, 114 Expanded Diagnosis Clusters (EDCs), and 68 Adjusted Clinical Groups (ACGs)). We found that, in general, for a given disease description system, counts of diseases and sets of disease dummy variables had similar explanatory power and that measures with more categories did better than those with fewer. The EDC measures performed best, followed by the QOF and ACG measures. The Charlson measures had the worst performance but still improved markedly on models containing only age, gender, deprivation and practice effects. Allowing for individual patient morbidity greatly reduced the association of age and cost. There was a pro-deprived bias in expenditure: after allowing for morbidity, patients in areas in the highest deprivation decile had costs which were 22% higher than those in the lowest deprivation decile. The predictive ability of the best performing morbidity and multimorbidity measures was very good for this type of individual level cross section data, with R2 ranging from 0.31 to 0.46. The statistical method of estimating the relationship between patient characteristics and costs was less important than the type of morbidity measure. Rankings of the morbidity and multimorbidity measures were broadly similar for generalised linear models with log link and Poisson errors and for OLS estimation. It would be currently feasible to combine the results from our study with the data on the number of patients with each QOF disease, which is available on all practices in England, to calculate budgets for general practices to cover their primary care costs.multimorbidity; primary care; utilisation; costs; deprivation; budgets