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Following excited-state chemical shifts in molecular ultrafast x-ray photoelectron spectroscopy
(2022)
The conversion of photon energy into other energetic forms in molecules is accompanied by charge moving on ultrafast timescales. We directly observe the charge motion at a specific site in an electronically excited molecule using time-resolved x-ray photoelectron spectroscopy (TR-XPS). We extend the concept of static chemical shift from conventional XPS by the excited-state chemical shift (ESCS), which is connected to the charge in the framework of a potential model. This allows us to invert TR-XPS spectra to the dynamic charge at a specific atom. We demonstrate the power of TR-XPS by using sulphur 2p-core-electron-emission probing to study the UV-excited dynamics of 2-thiouracil. The method allows us to discover that a major part of the population relaxes to the molecular ground state within 220–250 fs. In addition, a 250-fs oscillation, visible in the kinetic energy of the TR-XPS, reveals a coherent exchange of population among electronic states.
Following excited-state chemical shifts in molecular ultrafast x-ray photoelectron spectroscopy
(2022)
The conversion of photon energy into other energetic forms in molecules is accompanied by charge moving on ultrafast timescales. We directly observe the charge motion at a specific site in an electronically excited molecule using time-resolved x-ray photoelectron spectroscopy (TR-XPS). We extend the concept of static chemical shift from conventional XPS by the excited-state chemical shift (ESCS), which is connected to the charge in the framework of a potential model. This allows us to invert TR-XPS spectra to the dynamic charge at a specific atom. We demonstrate the power of TR-XPS by using sulphur 2p-core-electron-emission probing to study the UV-excited dynamics of 2-thiouracil. The method allows us to discover that a major part of the population relaxes to the molecular ground state within 220–250 fs. In addition, a 250-fs oscillation, visible in the kinetic energy of the TR-XPS, reveals a coherent exchange of population among electronic states.
X-ray computed tomography has many applications in materials science and non-destructive testing. While the standard filtered back-projection reconstruction of the radiographic datasets is fast and simple, it typically fails in returning accurate results from missing or inconsistent projections. Among the alternative techniques that have been proposed to handle such data is the Direct Iterative REconstruction of Computed Tomography Trajectories (DIRECTT) algorithm. We describe a new approach to the algorithm, which significantly decreases the computational time while achieving a better reconstruction quality than that of other established algorithms.
Levy walks (LWs) are spatiotemporally coupled random-walk processes describing superdiffusive heat conduction in solids, propagation of light in disordered optical materials, motion of molecular motors in living cells, or motion of animals, humans, robots, and viruses. We here investigate a key feature of LWs-their response to an external harmonic potential. In this generic setting for confined motion we demonstrate that LWs equilibrate exponentially and may assume a bimodal stationary distribution. We also show that the stationary distribution has a horizontal slope next to a reflecting boundary placed at the origin, in contrast to correlated superdiffusive processes. Our results generalize LWs to confining forces and settle some longstanding puzzles around LWs.
Machine learning control
(2020)
Recently, the term explainable AI came into discussion as an approach to produce models from artificial intelligence which allow interpretation. For a long time, symbolic regression has been used to produce explainable and mathematically tractable models. In this contribution, we extend previous work on symbolic regression methods to infer the optimal control of a dynamical system given one or several optimization criteria, or cost functions. In earlier publications, network control was achieved by automated machine learning control using genetic programming. Here, we focus on the subsequent path continuation analysis of the mathematical expressions which result from the machine learning model. In particular, we use AUTO to analyze the solution properties of the controlled oscillator system which served as our model. As a result, we show that there is a considerable advantage of explainable symbolic regression models over less accessible neural networks. In particular, the roadmap of future works may be to integrate such analyses into the optimization loop itself to filter out robust solutions by construction.
The Colorado Learning Attitudes about Science Survey (CLASS) is an instrument which is widely used in physics education to characterize students' attitudes toward physics and learning physics and compare them with those of experts. While CLASS has been extensively validated for use in the context of higher education institutions in the United States, there has been less information about its use with European students. We have studied the structural, content, and substantive aspects of validity of CLASS by first doing a confirmatory factor analysis of N = 642 sets of student answers from the University of Helsinki, Finland. The students represented a culturally and demographically different subset of university physics students than in previous studies. The confirmatory factor analysis used a 3-factor, 15-item factor structure as a starting point and the resulting factor structure was similar to the original. Just minor modifications were needed for fit parameters to be in the acceptable range. We explored the differences by student interviews and consultation of experts. With the exception of one item, they supported the new 14-item, 3-factor structure. The results show that the interpretations made from CLASS results are mostly transferable, and CLASS remains a useful instrument for a wide variety of populations.
This paper presents a study of the surface properties of two Ce/Zr mixed oxides with different reducibility, obtained by applying distinct thermal ageing treatments to an oxide with the composition Ce0.62Zr0.38O2. The surface composition was investigated by XPS. Chemical reactivity of the surface was studied by adsorption of the probe molecules CO2, D-2 and methanol. Nanostructural characterization was carried out by XRD, Raman and high-resolution Eu3+ spectroscopy (FLNS). The characterization showed only slight variations in surface composition and bulk Ce-Zr distribution, but hardy differences concerning the type and strength of acidic surface centres, as well as strong differences in the ability to dissociate hydrogen. Structural variations between both samples were identified by comparing the optical spectra of Eu3+ in surface doped samples.
Here, piezoelectric transducers consisting of a P(VDF-TrFE) layer with either silver or PEDOT:PSS screen-printed electrodes are studied. The influence of electrodes on the dielectric and electroacoustic properties are studied in dielectric-spectroscopy and ferroelectric-hysteresis measurements. Only when both the bottom and the top electrodes are made of silver, the typical dielectric relaxation of the P(VDF-TrFE) layer is clearly observed. When one or two of the electrodes are of PEDOT:PSS, a Debye-like relaxation is present. Compared with silver electrodes, PEDOT:PSS electrodes allow for moderate self-healing. Consequently, samples with bottom and top PEDOT:PSS electrodes can be poled to saturation, while samples with silver electrodes can hardly be poled to saturation due to destructive electric breakdown. Acoustic transducer measurements show that silver electrodes facilitate higher and broader frequency operation, while PEDOT:PSS electrodes bring slightly lower total harmonic distortion. Overall, the acoustic performance shows no significant deviations between differently electroded samples so that silver electrodes do not offer any advantages for the transducers studied here due to their much higher tendency for destructive electric breakdown.
The contamination of barley by molds on the field or in storage leads to the spoilage of grain and the production of mycotoxins, which causes major economic losses in malting facilities and breweries. Therefore, on-site detection of hidden fungus contaminations in grain storages based on the detection of volatile marker compounds is of high interest. In this work, the volatile metabolites of 10 different fungus species are identified by gas chromatography (GC) combined with two complementary mass spectrometric methods, namely, electron impact (EI) and chemical ionization at atmospheric pressure (APCI)-mass spectrometry (MS). The APCI source utilizes soft X-radiation, which enables the selective protonation of the volatile metabolites largely without side reactions. Nearly 80 volatile or semivolatile compounds from different substance classes, namely, alcohols, aldehydes, ketones, carboxylic acids, esters, substituted aromatic compounds, alkenes, terpenes, oxidized terpenes, sesquiterpenes, and oxidized sesquiterpenes, could be identified. The profiles of volatile and semivolatile metabolites of the different fungus species are characteristic of them and allow their safe differentiation. The application of the same GC parameters and APCI source allows a simple method transfer from MS to ion mobility spectrometry (IMS), which permits on-site analyses of grain stores. Characterization of IMS yields limits of detection very similar to those of APCI-MS. Accordingly, more than 90% of the volatile metabolites found by APCI-MS were also detected in IMS. In addition to different fungus genera, different species of one fungus genus could also be differentiated by GC-IMS.
In this chapter we review the basic principles of photocurrent generation in bulk heterojunction organic solar cells, discuss the loss channels limiting their efficiency, and present case studies of several polymer-fullerene blends. Using steady-state and transient, optical, and electrooptical techniques, we create a precise picture of the fundamental processes that ultimately govern solar cell efficiency.