| 1. |
- Abelev, B., et al.
(författare)
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Technical Design Report for the Upgrade of the ALICE Inner Tracking System
- 2014
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Ingår i: Journal of Physics G: Nuclear and Particle Physics. - : IOP Publishing. - 0954-3899 .- 1361-6471. ; 41:8
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Tidskriftsartikel (refereegranskat)abstract
- LICE (A Large Ion Collider Experiment) is studying the physics of strongly interacting matter, and in particular the properties of the Quark–Gluon Plasma (QGP), using proton–proton, proton–nucleus and nucleus–nucleus collisions at the CERN LHC (Large Hadron Collider). The ALICE Collaboration is preparing a major upgrade of the experimental apparatus, planned for installation in the second long LHC shutdown in the years 2018–2019. A key element of the ALICE upgrade is the construction of a new, ultra-light, high-resolution Inner Tracking System (ITS) based on monolithic CMOS pixel detectors. The primary focus of the ITS upgrade is on improving the performance for detection of heavy-flavour hadrons, and of thermal photons and low-mass di-electrons emitted by the QGP. With respect to the current detector, the new Inner Tracking System will significantly enhance the determination of the distance of closest approach to the primary vertex, the tracking efficiency at low transverse momenta, and the read-out rate capabilities. This will be obtained by seven concentric detector layers based on a 50 μm thick CMOS pixel sensor with a pixel pitch of about 30×30 μm2. This document, submitted to the LHCC (LHC experiments Committee) in September 2013, presents the design goals, a summary of the R&D activities, with focus on the technical implementation of the main detector components, and the projected detector and physics performance.
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| 2. |
- Schwarz, E, et al.
(författare)
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Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder
- 2019
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Ingår i: Translational psychiatry. - : Springer Science and Business Media LLC. - 2158-3188. ; 9:1, s. 12-
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Tidskriftsartikel (refereegranskat)abstract
- Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients.
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| 3. |
- Panchal, V., et al.
(författare)
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Mechanical Properties of Organic Electronic Polymers on the Nanoscale
- 2022
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Ingår i: Advanced Electronic Materials. - : Wiley. - 2199-160X. ; 8:3
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Tidskriftsartikel (refereegranskat)abstract
- Organic semiconducting polymers have attractive electronic, optical, and mechanical properties that make them materials of choice for large area flexible electronic devices. In these devices, the electronically active polymer components are micrometers in size, and sport negligible performance degradation upon bending the centimeter-scale flexible substrate onto which they are integrated. A closer look at the mechanical properties of the polymers, on the grain-scale and smaller, is not necessary in large area electronic applications. In emerging micromechanical and electromechanical applications where the organic polymer elements are flexed on length scales spanning their own micron-sized active areas, it becomes important to characterize the uniformity of their mechanical properties on the nanoscale. In this work, the authors use two precision nanomechanical characterization techniques, namely, atomic force microscope based PeakForce quantitative nanomechanical mapping (PF-QNM) and nanoindentation-based dynamical mechanical analysis (nano-DMA), to compare the modulus and the viscoelastic properties of organic polymers used routinely in organic electronics. They quantitatively demonstrate that the semiconducting near-amorphous organic polymer indacenodithiophene-co-benzothiadiazole (C16-IDTBT) has a higher carrier mobility, lower modulus, and greater nanoscale modulus areal uniformity compared to the semiconducting semicrystalline organic polymer poly[2,5-bis(3-tetradecylthiophen-2-yl)thieno[3,2-b]thiophene] (C14-PBTTT). Modulus homogeneity appears intrinsic to C16-IDTBT but can be improved in C14-PBTTT upon chemical doping.
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