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Effects of the incorporation of alkali elements on Cu(In,Ga)Se<sub>2</sub> thin film solar cells
Shin, Donghyeop,Kim, Jekyung,Gershon, Talia,Mankad, Ravin,Hopstaken, Marinus,Guha, Supratik,Ahn, Byung Tae,Shin, Byungha Elsevier 2016 Solar energy materials and solar cells Vol.157 No.-
<P><B>Abstract</B></P> <P>This study describes in detail the effects of sodium and potassium on Cu(In,Ga)Se<SUB>2</SUB> (CIGS) absorbers and solar cells. We report on the influence of these species on the surface and bulk composition as well as bulk defect structure of CIGS films as revealed by X-ray photoelectron spectroscopy (XPS), secondary ion mass spectroscopy (SIMS), and photoluminescence (PL). From the XPS studies it is found that Na and K promote oxygen absorption onto the CIGS films. Furthermore, potassium accelerates the formation of indium and gallium oxides on the film surface, making the surface Cu-deficient. Low temperature PL studies suggest that (i) Na and K help passivate non-radiative recombination centers, presumably at the grain boundaries, and (ii) Na further impacts the bulk defect structure inside of CIGS grains, which is not observed with K. This change in bulk defect structure is attributed to the greater diffusivity of Na in CIGS relative to K due to the smaller atomic size. This in-depth study (integration of XPS, SIMS, PL, and device characteristics) reveals that the surface chemistry and the grain boundary passivation have stronger influences on the device performance than the bulk defect structure.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Effects of alkali elements-Na and K-onCIGS are studied using SIMS, XPS, and PL. </LI> <LI> Alkali elements passivate non-radiative recombination centers at grain boundaries. </LI> <LI> The device with Na and K shows the highest efficiencies due to <I>surface</I> passivation. </LI> <LI> Passivation of both <I>external interfaces</I> and <I>internal grain boundaries</I> is crucial. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
Trimming offset surface self-intersections around near-singular regions
Hong, Q Youn,Park, Youngjin,Kim, Myung-Soo,Elber, Gershon Elsevier 2019 Computers & graphics Vol.82 No.-
<P><B>Abstract</B></P> <P>We present a new method for offset surface trimming that eliminates redundant parts of an offset surface that are closer than the offset distance to the original surface. The proposed approach deals with numerical instability around near-singular regions of an offset surface using the concept of offset trimming regions in the parameter space and carrying out numerical computations based on the regularity and intrinsic properties of the given input surface. In particular, we replace the self-intersection of an offset surface (which can be unstable around near-singular regions) by computation on the original input surface (and its derivatives) only, and also by the intersection of osculating tori that can be constructed in a highly stable way by offsetting the osculating tori of the given input regular surface. We demonstrate the effectiveness of our approach using non-trivial test examples of offset surface trimming, including some examples from the previous publications for the purpose of comparison.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A new algorithm for offset surface trimming that eliminates redundant parts of an offset surface that are closer than the offset distance to the original surface. </LI> <LI> A new approach that can deal with numerical instability around near-singular regions of an offset surface. </LI> <LI> A unique approach that can handle offset trimming without using offset surface. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
Danielle Jeddah,Ofer Chen,Ari M. Lipsky,Andrea Forgacs,Gershon Celniker,Craig M. Lilly,Itai M. Pessach 대한의료정보학회 2021 Healthcare Informatics Research Vol.27 No.3
Objectives: Predictive models for critical events in the intensive care unit (ICU) might help providers anticipate patient deterioration. At the heart of predictive model development lies the ability to accurately label significant events, thereby facilitating the use of machine learning and similar strategies. We conducted this study to establish the validity of an automated system for tagging respiratory and hemodynamic deterioration by comparing automatic tags to tagging by expert reviewers. Methods: This retrospective cohort study included 72,650 unique patient stays collected from Electronic Medical Records of the University of Massachusetts’ eICU. An enriched subgroup of stays was manually tagged by expert reviewers. The tags generated by the reviewers were compared to those generated by an automated system. Results: The automated system was able to rapidly and efficiently tag the complete database utilizing available clinical data. The overall agreement rate between the automated system and the clinicians for respiratory and hemodynamic deterioration tags was 89.4% and 87.1%, respectively. The automatic system did not add substantial variability beyond that seen among the reviewers. Conclusions: We demonstrated that a simple rule-based tagging system could provide a rapid and accurate tool for mass tagging of a compound database. These types of tagging systems may replace human reviewers and save considerable resources when trying to create a validated, labeled database used to train artificial intelligence algorithms. The ability to harness the power of artificial intelligence depends on efficient clinical validation of targeted conditions; hence, these systems and the methodology used to validate them are crucial.