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The Covid-19 pandemic has challenged educators across the world to move their teaching and mentoring from in-person to remote. During nonpandemic semesters at their institutes (e.g. universities), educators can directly provide students the software environment needed to support their learning - either in specialized computer laboratories (e.g. computational chemistry labs) or shared computer spaces. These labs are often supported by staff that maintains the operating systems (OS) and software. But how does one provide a specialized software environment for remote teaching? One solution is to provide students a customized operating system (e.g., Linux) that includes open-source software for supporting your teaching goals. However, such a solution should not require students to install the OS alongside their existing one (i.e. dual/multi-booting) or be used as a complete replacement. Such approaches are risky because of a) the students' possible lack of software expertise, b) the possible disruption of an existing software workflow that is needed in other classes or by other family members, and c) the importance of maintaining a working computer when isolated (e.g. societal restrictions). To illustrate possible solutions, we discuss our approach that used a customized Linux OS and a Docker container in a course that teaches computational chemistry and Python3.
Quantum mechanical theories are used to search and optimized the conformations of proposed small molecule candidates for treatment of SARS-CoV-2. These candidate compounds are taken from what is reported in the news and in other pre-peer-reviewed literature (e.g. ChemRxiv, bioRxiv). The goal herein is to provided predicted structures and relative conformational stabilities for selected drug and ligand candidates, in the hopes that other research groups can make use of them for developing a treatment.
In an effort to assist researchers in choosing basis sets for quantum mechanical modeling of molecules (i.e. balancing calculation cost versus desired accuracy), we present a systematic study on the accuracy of computed conformational relative energies and their geometries in comparison to MP2/CBS and MP2/AV5Z data, respectively. In order to do so, we introduce a new nomenclature to unambiguously indicate how a CBS extrapolation was computed. Nineteen minima and transition states of buta-1,3-diene, propan-2-ol and the water dimer were optimized using forty-five different basis sets. Specifically, this includes one Pople (i.e. 6-31G(d)), eight Dunning (i.e. VXZ and AVXZ, X=2-5), twenty-five Jensen (i.e. pc-n, pcseg-n, aug-pcseg-n, pcSseg-n and aug-pcSseg-n, n=0-4) and nine Karlsruhe (e.g. def2-SV(P), def2-QZVPPD) basis sets. The molecules were chosen to represent both common and electronically diverse molecular systems. In comparison to MP2/CBS relative energies computed using the largest Jensen basis sets (i.e. n=2,3,4), the use of smaller sizes (n=0,1,2 and n=1,2,3) provides results that are within 0.11--0.24 and 0.09-0.16 kcal/mol. To practically guide researchers in their basis set choice, an equation is introduced that ranks basis sets based on a user-defined balance between their accuracy and calculation cost. Furthermore, we explain why the aug-pcseg-2, def2-TZVPPD and def2-TZVP basis sets are very suitable choices to balance speed and accuracy.
Energy Profiles of the Ring Puckering of Cyclopentane, Methylcyclopentane and Ethylcyclopentane
(2019)
The elucidation of conformations and relative potential energies (rPEs) of small molecules has a long history across a diverse range of fields. Periodically, it is helpful to revisit what conformations have been investigated and to provide a consistent theoretical framework for which clear comparisons can be made. In this paper, we compute the minima, first- and second-order saddle points, and torsion-coupled surfaces for methanol, ethanol, propan-2-ol, and propanol using consistent high-level MP2 and CCSD(T) methods. While for certain molecules more rigorous methods were employed, the CCSD(T)/aug-cc-pVTZ//MP2/aug-cc-pV5Z theory level was used throughout to provide relative energies of all minima and first-order saddle points. The rPE surfaces were uniformly computed at the CCSD(T)/aug-cc-pVTZ//MP2/aug-cc-pVTZ level. To the best of our knowledge, this represents the most extensive study for alcohols of this kind, revealing some new aspects. Especially for propanol, we report several new conformations that were previously not investigated. Moreover, two metrics are included in our analysis that quantify how the selected surfaces are similar to one another and hence improve our understanding of the relationship between these alcohols.
This paper describes adaptive time frequency analysis of EEG signals, both in theory as well as in practice. A momentary frequency estimation algorithm is discussed and applied to EEG time series of test persons performing a concentration experiment. The motivation for deriving and implementing a time frequency estimator is the assumption that an emotional change implies a transient in the measured EEG time series, which again are superimposed by biological white noise as well as artifacts. It will be shown how accurately and robustly the estimator detects the transient even under such complicated conditions.
In this paper, we describe an approach to academic teaching in computer science using storytelling as a means to investigate to hypermedia and virtual reality topics. Indications are shown that narrative activity within the context of a Hypermedia Novel related to educational content can enhance motivation for self-conducted learning and in parallel lead to an edutainment system of its own. In contrast to existing approaches the Hypermedia Novel environment allows an iterative approach to the narrative content, thereby integrating story authoring and story reception not only in the beginning but at any time. The narrative practice and background research as well as the resulting product can supplement lecture material with comparable success to traditional academic teaching approaches. On top of this there is the added value of soft skill training and a gain of expert knowledge in areas of personal background research.
Simultaneous detection of cyanide and heavy metals for environmental analysis by means of µISEs
(2010)
The objective of the FIVIS project is to develop a bicycle simulator which is able to simulate real life bicycle ride situations as a virtual scenario within an immersive environment. A sample test bicycle is mounted on a motion platform to enable a close to reality simulation of turns and balance situations. The visual field of the bike rider is enveloped within a multi-screen visualisation environment which provides visual data relative to the motion and activity of the test bicycle. That means the bike rider has to pedal and steer the bicycle as a usual bicycle, while the motion is recorded and processed to control the simulation. Furthermore, the platform is fed with real forces and accelerations that have been logged by a mobile data acquisition system during real bicycle test drives. Thus, using a feedback system makes the movements of the platform match to the virtual environment and the reaction of the driver (e.g. steering angle, step rate).