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| 2. |
- Milne, Christopher J., et al.
(författare)
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Disentangling the evolution of electrons and holes in photoexcited ZnO nanoparticles
- 2023
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Ingår i: Structural Dynamics. - : American Institute of Physics (AIP). - 2329-7778. ; 10:6
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Tidskriftsartikel (refereegranskat)abstract
- The evolution of charge carriers in photoexcited room temperature ZnO nanoparticles in solution is investigated using ultrafast ultraviolet photoluminescence spectroscopy, ultrafast Zn K-edge absorption spectroscopy, and ab initio molecular dynamics (MD) simulations. The photoluminescence is excited at 4.66 eV, well above the band edge, and shows that electron cooling in the conduction band and exciton formation occur in <500 fs, in excellent agreement with theoretical predictions. The x-ray absorption measurements, obtained upon excitation close to the band edge at 3.49 eV, are sensitive to the migration and trapping of holes. They reveal that the 2 ps transient largely reproduces the previously reported transient obtained at 100 ps time delay in synchrotron studies. In addition, the x-ray absorption signal is found to rise in similar to 1.4 ps, which we attribute to the diffusion of holes through the lattice prior to their trapping at singly charged oxygen vacancies. Indeed, the MD simulations show that impulsive trapping of holes induces an ultrafast expansion of the cage of Zn atoms in <200 fs, followed by an oscillatory response at a frequency of similar to 100 cm-1, which corresponds to a phonon mode of the system involving the Zn sub-lattice.
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| 3. |
- Northey, Thomas, et al.
(författare)
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Ultrafast nonadiabatic dynamics probed by nitrogen K-edge absorption spectroscopy
- 2020
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Ingår i: Physical Chemistry, Chemical Physics - PCCP. - 1463-9076 .- 1463-9084. ; 22:5, s. 2667-2676
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Tidskriftsartikel (refereegranskat)abstract
- The emergence of X-ray free electron lasers (X-FELs) has made it possible to probe structural dynamics on the femtosecond timescale. This extension of experimental capabilities also calls for a simultaneous development in theory to help interpret the underlying structure and dynamics encoded within the experimental observable. In the ultrafast regime this often requires a time-dependent theoretical treatment that describes nuclear dynamics beyond the Born–Oppenheimer approximation. In this work, we perform quantum dynamics simulations based upon time-evolving Gaussian basis functions (GBFs) and simulate the ultrafast X-ray Absorption Near-Edge Structure (XANES) spectra of photoexcited pyrazine including two strongly coupled electronically excited states and four normal mode degrees of freedom. Two methods to simulate the excited state XANES spectra are applied, the first is based upon the multi-configurational second order perturbation theory restricted active space (RASPT2) method and the second uses a combination of the maximum overlap method (MOM) and time-dependent density functional theory (TDDFT). We demonstrate that despite the simplicity of the MOM/TDDFT method, it captures several qualitative features of the RASPT2 simulations at much reduced computational effort. However, features such as the conical intersection are a particular exception as they require a multi-configurational treatment. For the nuclear dynamics, we demonstrate that even a small number of GBFs can provide reasonable description of the spectroscopic observable. This work provides perspectives for computationally efficient approaches important for addressing larger systems.
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| 4. |
- Watson, Luke, et al.
(författare)
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A Δ”-learning strategy for interpretation of spectroscopic observables
- 2023
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Ingår i: Structural Dynamics. - : American Institute of Physics (AIP). - 2329-7778. ; 10:6
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Tidskriftsartikel (refereegranskat)abstract
- Accurate computations of experimental observables are essential for interpreting the high information content held within x-ray spectra. However, for complicated systems this can be difficult, a challenge compounded when dynamics becomes important owing to the large number of calculations required to capture the time-evolving observable. While machine learning architectures have been shown to represent a promising approach for rapidly predicting spectral lineshapes, achieving simultaneously accurate and sufficiently comprehensive training data is challenging. Herein, we introduce Delta-learning for x-ray spectroscopy. Instead of directly learning the structure-spectrum relationship, the Delta-model learns the structure dependent difference between a higher and lower level of theory. Consequently, once developed these models can be used to translate spectral shapes obtained from lower levels of theory to mimic those corresponding to higher levels of theory. Ultimately, this achieves accurate simulations with a much reduced computational burden as only the lower level of theory is computed, while the model can instantaneously transform this to a spectrum equivalent to a higher level of theory. Our present model, demonstrated herein, learns the difference between TDDFT(BLYP) and TDDFT(B3LYP) spectra. Its effectiveness is illustrated using simulations of Rh L-3-edge spectra tracking the C-H activation of octane by a cyclopentadienyl rhodium carbonyl complex.
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| 5. |
- Sudre, Carole H., et al.
(författare)
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Attributes and predictors of long COVID
- 2021
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Ingår i: Nature Medicine. - : Springer Nature. - 1078-8956 .- 1546-170X. ; 27:4, s. 626-631
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Tidskriftsartikel (refereegranskat)abstract
- Reports of long-lasting coronavirus disease 2019 (COVID-19) symptoms, the so-called ‘long COVID’, are rising but little is known about prevalence, risk factors or whether it is possible to predict a protracted course early in the disease. We ana- lyzed data from 4,182 incident cases of COVID-19 in which individuals self-reported their symptoms prospectively in the COVID Symptom Study app1. A total of 558 (13.3%) partici- pants reported symptoms lasting ≥28 days, 189 (4.5%) for ≥8 weeks and 95 (2.3%) for ≥12 weeks. Long COVID was characterized by symptoms of fatigue, headache, dyspnea and anosmia and was more likely with increasing age and body mass index and female sex. Experiencing more than five symptoms during the first week of illness was associated with long COVID (odds ratio = 3.53 (2.76–4.50)). A simple model to distinguish between short COVID and long COVID at 7 days (total sample size, n = 2,149) showed an area under the curve of the receiver operating characteristic curve of 76%, with replication in an independent sample of 2,472 individuals who were positive for severe acute respiratory syndrome coronavi- rus 2. This model could be used to identify individuals at risk of long COVID for trials of prevention or treatment and to plan education and rehabilitation services.
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