Analysis of defect-related optical degradation of VCSILs for photonic integrated circuits

Laser diodes are of paramount importance for on-chip telecommunications applications, and a wide range of sensing devices that require near-infrared sources. In this work, the devices under test are vertical-cavity silicon-integrated lasers (VCSILs) designed for operation at 845 nm in photonic integrated circuits (PICs). We focus on the analysis of the degradation of the optical performance during aging. To investigate the reliability of the devices, we carried out several stress tests at constant current, ranging from 3.5 mA to 4.5 mA representing a highly accelerated stress condition. We observed two different degradation modes. In the first part of the experiments, the samples exhibited a worsening of the threshold current, but the sub-threshold emission was unaffected by degradation. We associated this behavior to the diffusion of impurities that, from the p-contact, were crossing the upper mirror implying a worsening of the DBR optical absorption. In the second stage of the stress test, the devices showed a higher degradation rate of the threshold current, whose variation was found to be linearly correlated to the worsening of the sub-threshold emission. We related this second degradation mode to the migration of the same impurities degrading the top DBR that, when reaching the active region of the laser, induced an increase in the non-radiative recombination rate. In addition to that, we related the two degradation modes to the change in series resistance, which was ascribed to the resistivity increment of the top DBR first and of oxide aperture afterwards

Modeling the electrical characteristics of InGaN/GaN LED structures based on experimentally-measured defect characteristics

Defects can significantly modify the electro-optical characteristics of InGaN light-emitting diodes (LEDs); however, modeling the impact of defects on the electrical characteristics of LEDs is not straightforward. In this paper, we present an extensive investigation and modeling of the impact of defects on the electrical characteristics of InGaN-based LEDs, as a function of the thickness of the quantum well (QW). First, we demonstrate that the density of defects in the active region of III-N LEDs scales with increasing thickness of the InGaN QW. Since device layers with high indium content tend to incorporate more defects, we ascribed this experimental evidence to the increased volume of defects-rich InGaN associated to thicker InGaN layers. Second, we demonstrate that the current-voltage characteristics of the devices are significantly influenced by the presence of defects, especially in the sub turn-on region. Specifically, we show that the electrical characteristics can be effectively modeled in a wide current range (from pA to mA), by considering the existence of trap-assisted tunneling processes. A good correspondence is obtained between the experimental and simulated electrical characteristics (I-V), by using-in the simulation-the actual defect concentrations/activation energies extracted from steady-state photocapacitance, instead of generic fitting parameters

PDGF enhances the protective effect of adipose stem cell-derived extracellular vesicles in a model of acute hindlimb ischemia

Abstract We previously have shown that platelet-derived growth factor (PDGF) modulates the biological activity of extracellular vesicles released by adipose-derived mesenchymal stem cells (ASC-EVs). ASC-EVs may interact with blood and vessel cells by transferring proteins and nucleic acids and regulate their functions. In this study, we investigated immunomodulatory activity and protection from acute hindlimb ischemia of EVs released by PDGF-stimulated ASC (PDGF-EVs). PDGF treatment of ASC changed protein and RNA composition of released EVs by enhancing the expression of anti-inflammatory and immunomodulatory factors. In vitro, control EVs (cEVs) derived from non-stimulated ASC increased the secretion of both the IL-1b, IL-17, IFNγ, TNFα pro-inflammatory factors and the IL-10 anti-inflammatory factor, and enhanced the in vitro peripheral blood mononuclear cell (PBMC) adhesion on endothelium. In contrast, PDGF-EVs enhanced IL-10 secretion and induced TGF-β1 secretion by PBMC. Moreover, PDGF-EVs stimulated the formation of T regulatory cells. In vivo, PDGF-EVs protected muscle tissue from acute ischemia, reduced infiltration of inflammatory cells and increased T regulatory cell infiltration in respect to cEVs. Our results suggest that PDGF-EVs are enriched in anti-inflammatory and immunomodulatory factors and induced in PBMC an enhanced production of IL-10 and TGF-β1 resulting in protection of muscle from acute ischemia in vivo

Use of Steroid Profiling Combined With Machine Learning for Identification and Subtype Classification in Primary Aldosteronism

Importance: Most patients with primary aldosteronism, a major cause of secondary hypertension, are not identified or appropriately treated because of difficulties in diagnosis and subtype classification. Applications of artificial intelligence combined with mass spectrometry–based steroid profiling could address this problem. Objective: To assess whether plasma steroid profiling combined with machine learning might facilitate diagnosis and treatment stratification of primary aldosteronism, particularly for patients with unilateral adenomas due to pathogenic KCNJ5 sequence variants. Design, Setting, and Participants: This diagnostic study was conducted at multiple tertiary care referral centers. Steroid profiles were measured from June 2013 to March 2017 in 462 patients tested for primary aldosteronism and 201 patients with hypertension. Data analyses were performed from September 2018 to August 2019. Main Outcomes and Measures: The aldosterone to renin ratio and saline infusion tests were used to diagnose primary aldosteronism. Subtyping was done by adrenal venous sampling and follow-up of patients who underwent adrenalectomy. Statistical tests and machine-learning algorithms were applied to plasma steroid profiles. Areas under receiver operating characteristic curves, sensitivity, specificity, and other diagnostic performance measures were calculated. Results: Primary aldosteronism was confirmed in 273 patients (165 men [60%]; mean [SD] age, 51 [10] years), including 134 with bilateral disease and 139 with unilateral adenomas (58 with and 81 without somatic KCNJ5 sequence variants). Plasma steroid profiles varied according to disease subtype and were particularly distinctive in patients with adenomas due to KCNJ5 variants, who showed better rates of biochemical cure after adrenalectomy than other patients. Among patients tested for primary aldosteronism, a selection of 8 steroids in combination with the aldosterone to renin ratio showed improved effectiveness for diagnosis over either strategy alone. In contrast, the steroid profile alone showed superior performance over the aldosterone to renin ratio for identifying unilateral disease, particularly adenomas due to KCNJ5 variants. Among 632 patients included in the analysis, machine learning–designed combinatorial marker profiles of 7 steroids alone both predicted primary aldosteronism in 1 step and subtyped patients with unilateral adenomas due to KCNJ5 variants at diagnostic sensitivities of 69% (95% CI, 68%-71%) and 85% (95% CI, 81%-88%), respectively, and at specificities of 94% (95% CI, 93%-94%) and 97% (95% CI, 97%-98%), respectively. The validation series yielded comparable diagnostic performance. Conclusions and Relevance: Machine learning–designed combinatorial plasma steroid profiles may facilitate both screening for primary aldosteronism and identification of patients with unilateral adenomas due to pathogenic KCNJ5 variants, who are most likely to show benefit from surgical intervention

Defects in III-N LEDs: experimental identification and impact on electro-optical characteristics

III-N light-emitting-diodes (LEDs) are subject of intense investigations, thanks to their high efficiency and great reliability. The quality of the semiconductor material has a significant impact on the electro-optical performance of LEDs: for this reason, a detailed characterization of defect properties and the modeling of the impact of defects on device performance are of fundamental importance. This presentation addresses this issue, by discussing a set of recent case studies on the topic; specifically, we focus on the experimental characterization of defects, and on the modeling of their impact on the electro-optical characteristics of the devices

Nanoanalytical analysis of bisphosphonate-driven alterations of microcalcifications using a 3D hydrogel system and in vivo mouse model

Vascular calcification predicts atherosclerotic plaque rupture and cardiovascular events. Retrospective studies of women taking bisphosphonates (BiPs), a proposed therapy for vascular calcification, showed that BiPs paradoxically increased morbidity in patients with prior acute cardiovascular events but decreased mortality in event-free patients. Calcifying extracellular vesicles (EVs), released by cells within atherosclerotic plaques, aggregate and nucleate calcification. We hypothesized that BiPs block EV aggregation and modify existing mineral growth, potentially altering microcalcification morphology and the risk of plaque rupture. Three-dimensional (3D) collagen hydrogels incubated with calcifying EVs were used to mimic fibrous cap calcification in vitro, while an ApoE-/- mouse was used as a model of atherosclerosis in vivo. EV aggregation and formation of stress-inducing microcalcifications was imaged via scanning electron microscopy (SEM) and atomic force microscopy (AFM). In both models, BiP (ibandronate) treatment resulted in time-dependent changes in microcalcification size and mineral morphology, dependent on whether BiP treatment was initiated before or after the expected onset of microcalcification formation. Following BiP treatment at any time, microcalcifications formed in vitro were predicted to have an associated threefold decrease in fibrous cap tensile stress compared to untreated controls, estimated using finite element analysis (FEA). These findings support our hypothesis that BiPs alter EV-driven calcification. The study also confirmed that our 3D hydrogel is a viable platform to study EV-mediated mineral nucleation and evaluate potential therapies for cardiovascular calcification