Strongly Enhanced Photovoltaic Performance and Defect Physics of Air-Stable Bismuth Oxyiodide (BiOI)

Bismuth-based compounds have recently gained increasing attention as potentially nontoxic and defect-tolerant solar absorbers. However, many of the new materials recently investigated show limited photovoltaic performance. Herein, one such compound is explored in detail through theory and experiment: bismuth oxyiodide (BiOI). BiOI thin films are grown by chemical vapor transport and found to maintain the same tetragonal phase in ambient air for at least 197 d. The computations suggest BiOI to be tolerant to antisite and vacancy defects. All-inorganic solar cells (ITO|NiO$_x$|BiOI|ZnO|Al) with negligible hysteresis and up to 80% external quantum efficiency under select monochromatic excitation are demonstrated. The short-circuit current densities and power conversion efficiencies under AM 1.5G illumination are nearly double those of previously reported BiOI solar cells, as well as other bismuth halide and chalcohalide photovoltaics recently explored by many groups. Through a detailed loss analysis using optical characterization, photoemission spectroscopy, and device modeling, direction for future improvements in efficiency is provided. This work demonstrates that BiOI, previously considered to be a poor photocatalyst, is promising for photovoltaics.R.L.Z.H. thanks Magdalene College, Cambridge. L.C.L. and J.L.M.-D. thank the EPRSC Centre for Doctoral Training: New and Sustainable Photovoltaics, and the Cambridge Winton Programme for the Physics of Sustainability for funding. T.N.H. thanks the Cambridge Graphene Centre, funded by the EPSRC. K.H.L.Z. was supported by the Herschel Smith fellowship. The U.S.-based theory and synthesis portions of this work were supported primarily as part of the Center for Next Generation Materials by Design (CNGMD), an Energy Frontier Research Center funded by the DOE Office of Science, Basic Energy Sciences under Contract No. DE-AC36-08GO28308. The MIT-based characterization portion of this work was supported primarily through a TOTAL SA research grant funded through MITei, as well as a SusChem grant funded by the National Science Foundation (No. CBET-1605495). The TCSPC work was supported as part of the Center for Excitonics, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Award No. DE-SC0001088 (MIT). The computations were performed using resources sponsored by the Department of Energy’s Office of Energy Efficiency and Renewable Energy and located at the NREL. The authors also acknowledge the MRSEC Shared Experimental Facilities at MIT, supported by the National Science Foundation (No. DMF-08019762)

Quantitative metric profiles capture three-dimensional temporospatial architecture to discriminate cellular functional states

<p>Abstract</p> <p>Background</p> <p>Computational analysis of tissue structure reveals sub-visual differences in tissue functional states by extracting quantitative signature features that establish a diagnostic profile. Incomplete and/or inaccurate profiles contribute to misdiagnosis.</p> <p>Methods</p> <p>In order to create more complete tissue structure profiles, we adapted our cell-graph method for extracting quantitative features from histopathology images to now capture temporospatial traits of three-dimensional collagen hydrogel cell cultures. Cell-graphs were proposed to characterize the spatial organization between the cells in tissues by exploiting graph theory wherein the nuclei of the cells constitute the <it>nodes </it>and the approximate adjacency of cells are represented with <it>edges</it>. We chose 11 different cell types representing non-tumorigenic, pre-cancerous, and malignant states from multiple tissue origins.</p> <p>Results</p> <p>We built cell-graphs from the cellular hydrogel images and computed a large set of features describing the structural characteristics captured by the graphs over time. Using three-mode tensor analysis, we identified the five most significant features (metrics) that capture the compactness, clustering, and spatial uniformity of the 3D architectural changes for each cell type throughout the time course. Importantly, four of these metrics are also the discriminative features for our histopathology data from our previous studies.</p> <p>Conclusions</p> <p>Together, these descriptive metrics provide rigorous quantitative representations of image information that other image analysis methods do not. Examining the changes in these five metrics allowed us to easily discriminate between all 11 cell types, whereas differences from visual examination of the images are not as apparent. These results demonstrate that application of the cell-graph technique to 3D image data yields discriminative metrics that have the potential to improve the accuracy of image-based tissue profiles, and thus improve the detection and diagnosis of disease.</p

Coupled Analysis of In Vitro and Histology Tissue Samples to Quantify Structure-Function Relationship

The structure/function relationship is fundamental to our understanding of biological systems at all levels, and drives most, if not all, techniques for detecting, diagnosing, and treating disease. However, at the tissue level of biological complexity we encounter a gap in the structure/function relationship: having accumulated an extraordinary amount of detailed information about biological tissues at the cellular and subcellular level, we cannot assemble it in a way that explains the correspondingly complex biological functions these structures perform. To help close this information gap we define here several quantitative temperospatial features that link tissue structure to its corresponding biological function. Both histological images of human tissue samples and fluorescence images of three-dimensional cultures of human cells are used to compare the accuracy of in vitro culture models with their corresponding human tissues. To the best of our knowledge, there is no prior work on a quantitative comparison of histology and in vitro samples. Features are calculated from graph theoretical representations of tissue structures and the data are analyzed in the form of matrices and higher-order tensors using matrix and tensor factorization methods, with a goal of differentiating between cancerous and healthy states of brain, breast, and bone tissues. We also show that our techniques can differentiate between the structural organization of native tissues and their corresponding in vitro engineered cell culture models

Intrapericardial Delivery of Gelfoam Enables the Targeted Delivery of Periostin Peptide after Myocardial Infarction by Inducing Fibrin Clot Formation

Background: Administration of a recombinant peptide of Periostin (rPN) has recently been shown to stimulate cardiomyocyte proliferation and angiogensis after myocardial infarction (MI). However, strategies for targeting the delivery of rPN to the heart are lacking. Intrapericardial administration of drug-eluting hydrogels may provide a clinically viable strategy for increasing myocardial retention, therapeutic efficacy, and bioactivity of rPN and to decrease systemic re-circulation. Methods and Results: We investigated the ability of intrapericardial injections of drug-eluting hydrogels to deliver and prolong the release of rPN to the myocardium in a large animal model of myocardial infarction. Gelfoam is an FDA-approved hemostatic material commonly used in surgery, and is known to stimulate fibrin clot formation. We show that Gelfoam disks loaded with rPN, when implanted within the pericardium or peritoneum of mammals becomes encapsulated within a non-fibrotic fibrin-rich hydrogel, prolonging the in vitro and in vivo release of rPN. Administration into the pericardial cavity of pigs, following a complete occlusion of the left anterior descending artery, leads to greater induction of cardiomyocyte mitosis, increased cardiomyocyte cell cycle activity, and enhanced angiogenesis compared to direct injection of rPN alone. Conclusions: The results of this study suggest that intrapericardial drug delivery of Gelfoam, enhanced by triggered clot formation, can be used to effectively deliver rPN to the myocardium in a clinically relevant model of myocardial infarction. The work presented here should enhance the translational potential of pharmaceutical-based strategies that must be targeted to the myocardium