The Stability and Manufacturability of Emerging Thin Film Photovoltaic Technologies

In order for a photovoltaic device to be commercially viable it must have a production cost and operational stability commensurate with its final application. Both of these properties are influenced by many factors, including the production of the active materials and the deposition techniques used to fabricate it. In this thesis, the stability and manufacturability of two emerging photovoltaic materials are examined: organic semiconducting polymers and organic-inorganic perovskites. Organic semiconducting polymers are commonly synthesised through reactions utilising metal catalysts, which can remain with the polymer after synthesis, necessitating the investigation of their influence on photovoltaic devices. This work shows that the presence of the residual catalyst palladium in PCDTBT organic photovoltaic (OPV) devices caused significant reductions in power conversion efficiency and an additional increase in efficiency loss during the first 60 hours of operation. It is also shown, however, that only minor losses occurred in PFD2TBT-8 OPV devices at high Pd concentrations, highlighting the need to examine individual material systems. Despite being a very new technology, perovskite solar cells (PSCs) have already achieved comparable performance to silicon solar cells, making it important to investigate the stability of such devices. The operational stability of PSCs in the inverted architecture was characterised, showing lifetimes of <300 hours. Using spectroscopic and device characterisation techniques, the major loss mechanisms were revealed to be reactions with water and oxygen, resulting the in the decomposition of the perovskite. It is also examined how the addition of hydroiodic acid to the perovskite precursor solution affects the performance and stability of spin and spray coated PSCs. Finally, the effects of deposition temperature and additional annealing on the operational stability of PSCs was investigated

Anti–GM-CSF otilimab versus sarilumab or placebo in patients with rheumatoid arthritis and inadequate response to targeted therapies: a phase III randomised trial (contRAst 3)

Objectives To investigate the efficacy and safety of otilimab, an anti-granulocyte-macrophage colony-stimulating factor antibody, in patients with active rheumatoid arthritis and an inadequate response to conventional synthetic (cs) and biologic disease-modifying antirheumatic drugs (DMARDs) and/or Janus kinase inhibitors. Methods ContRAst 3 was a 24-week, phase III, multicentre, randomised controlled trial. Patients received subcutaneous otilimab (90/150 mg once weekly), subcutaneous sarilumab (200 mg every 2 weeks) or placebo for 12 weeks, in addition to csDMARDs. Patients receiving placebo were switched to active interventions at week 12 and treatment continued to week 24. The primary end point was the proportion of patients achieving an American College of Rheumatology ≥20% response (ACR20) at week 12. Results Overall, 549 patients received treatment. At week 12, there was no significant difference in the proportion of ACR20 responders with otilimab 90 mg and 150 mg versus placebo (45% (p=0.2868) and 51% (p=0.0596) vs 38%, respectively). There were no significant differences in Clinical Disease Activity Index, Health Assessment Questionnaire-Disability Index, pain Visual Analogue Scale or Functional Assessment of Chronic Illness Therapy-Fatigue scores with otilimab versus placebo at week 12. Sarilumab demonstrated superiority to otilimab in ACR20 response and secondary end points. The incidence of adverse or serious adverse events was similar across treatment groups. Conclusions Otilimab demonstrated an acceptable safety profile but failed to achieve the primary end point of ACR20 and improve secondary end points versus placebo or demonstrate non-inferiority to sarilumab in this patient population. Trial registration number NCT04134728

Anti-GM-CSF otilimab versus tofacitinib or placebo in patients with active rheumatoid arthritis and an inadequate response to conventional or biologic DMARDs: two phase 3 randomised trials (contRAst 1 and contRAst 2)

Objectives To investigate the efficacy and safety of otilimab, an antigranulocyte-macrophage colony-stimulating factor antibody, in patients with active rheumatoid arthritis. Methods Two phase 3, double-blind randomised controlled trials including patients with inadequate responses to methotrexate (contRAst 1) or conventional synthetic/biologic disease-modifying antirheumatic drugs (cs/bDMARDs; contRAst 2). Patients received background csDMARDs. Through a testing hierarchy, subcutaneous otilimab (90/150 mg once weekly) was compared with placebo for week 12 endpoints (after which, patients receiving placebo switched to active interventions) or oral tofacitinib (5 mg two times per day) for week 24 endpoints. Primary endpoint: proportion of patients achieving an American College of Rheumatology response ≥20% (ACR20) at week 12. Results The intention-to-treat populations comprised 1537 (contRAst 1) and 1625 (contRAst 2) patients. Primary endpoint: proportions of ACR20 responders were statistically significantly greater with otilimab 90 mg and 150 mg vs placebo in contRAst 1 (54.7% (p=0.0023) and 50.9% (p=0.0362) vs 41.7%) and contRAst 2 (54.9% (p<0.0001) and 54.5% (p<0.0001) vs 32.5%). Secondary endpoints: in both trials, compared with placebo, otilimab increased the proportion of Clinical Disease Activity Index (CDAI) low disease activity (LDA) responders (not significant for otilimab 150 mg in contRAst 1), and reduced Health Assessment Questionnaire-Disability Index (HAQ-DI) scores. Benefits with tofacitinib were consistently greater than with otilimab across multiple endpoints. Safety outcomes were similar across treatment groups. Conclusions Although otilimab demonstrated superiority to placebo in ACR20, CDAI LDA and HAQ-DI, improved symptoms, and had an acceptable safety profile, it was inferior to tofacitinib. Trial registration numbers NCT03980483, NCT03970837

Validation of ozone measurements from the Atmospheric Chemistry Experiment (ACE)

This paper presents extensive bias determination analyses of ozone observations from the Atmospheric Chemistry Experiment (ACE) satellite instruments: the ACE Fourier Transform Spectrometer (ACE-FTS) and the Measurement of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation (ACE-MAESTRO) instrument. Here we compare the latest ozone data products from ACE-FTS and ACE-MAESTRO with coincident observations from nearly 20 satellite-borne, airborne, balloon-borne and ground-based instruments, by analysing volume mixing ratio profiles and partial column densities. The ACE-FTS version 2.2 Ozone Update product reports more ozone than most correlative measurements from the upper troposphere to the lower mesosphere. At altitude levels from 16 to 44 km, the average values of the mean relative differences are nearly all within +1 to +8%. At higher altitudes (45 60 km), the ACE-FTS ozone amounts are significantly larger than those of the comparison instruments, with mean relative differences of up to +40% (about + 20% on average). For the ACE-MAESTRO version 1.2 ozone data product, mean relative differences are within +/- 10% (average values within +/- 6%) between 18 and 40 km for both the sunrise and sunset measurements. At higher altitudes (similar to 35-55 km), systematic biases of opposite sign are found between the ACE-MAESTRO sunrise and sunset observations. While ozone amounts derived from the ACE-MAESTRO sunrise occultation data are often smaller than the coincident observations (with mean relative differences down to -10%), the sunset occultation profiles for ACE-MAESTRO show results that are qualitatively similar to ACE-FTS, indicating a large positive bias (mean relative differences within +10 to +30%) in the 45-55 km altitude range. In contrast, there is no significant systematic difference in bias found for the ACE-FTS sunrise and sunset measurements

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Purcell Enhancement of a Single Silicon Carbide Color Center with Coherent Spin Control

Silicon carbide has recently been developed as a platform for optically addressable spin defects. In particular, the neutral divacancy in the 4H polytype displays an optically addressable spin-1 ground state and near-infrared optical emission. Here, we present the Purcell enhancement of a single neutral divacancy coupled to a photonic crystal cavity. We utilize a combination of nanolithographic techniques and a dopant-selective photoelectrochemical etch to produce suspended cavities with quality factors exceeding 5000. Subsequent coupling to a single divacancy leads to a Purcell factor of ∼50, which manifests as increased photoluminescence into the zero-phonon line and a shortened excited-state lifetime. Additionally, we measure coherent control of the divacancy ground-state spin inside the cavity nanostructure and demonstrate extended coherence through dynamical decoupling. This spin-cavity system represents an advance toward scalable long-distance entanglement protocols using silicon carbide that require the interference of indistinguishable photons from spatially separated single qubits

Anti-folate receptor-α IgE but not IgG recruits macrophages to attack tumors via TNFa/MCP-1 signaling

IgE antibodies are key mediators of antiparasitic immune responses, but their potential for cancer treatment via antibody-dependent cell-mediated cytotoxicity (ADCC) has been little studied. Recently, tumor antigen–specific IgEs were reported to restrict cancer cell growth by engaging high-affinity Fc receptors on monocytes and macrophages; however, the underlying therapeutic mechanisms were undefined and in vivo proof of concept was limited. Here, an immunocompetent rat model was designed to recapitulate the human IgE-Fcε receptor system for cancer studies. We also generated rat IgE and IgG mAbs specific for the folate receptor (FRα), which is expressed widely on human ovarian tumors, along with a syngeneic rat tumor model expressing human FRα. Compared with IgG, anti-FRα IgE reduced lung metastases. This effect was associated with increased intratumoral infiltration by TNFα+ and CD80⁺ macrophages plus elevated TNFα and the macrophage chemoattractant MCP-1 in lung bronchoalveolar lavage fluid. Increased levels of TNFα and MCP-1 correlated with IgE-mediated tumor cytotoxicity by human monocytes and with longer patient survival in clinical specimens of ovarian cancer. Monocytes responded to IgE but not IgG exposure by upregulating TNFα, which in turn induced MCP-1 production by monocytes and tumor cells to promote a monocyte chemotactic response. Conversely, blocking TNFα receptor signaling abrogated induction of MCP-1, implicating it in the antitumor effects of IgE. Overall, these findings show how antitumor IgE reprograms monocytes and macrophages in the tumor microenvironment, encouraging the clinical use of IgE antibody technology to attack cancer beyond the present exclusive reliance on IgG