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Data repository for "Disentangling the Evolution of Electrons and Holes in photoexcited ZnO nanoparticles", submitted to Structural Dynamics (#SDY23-AR-XAS2023-00063). Data are divided in 4 numbered folders, organized as follows: Folder "1 - ZnO_PL_scans": This folder contains the raw data of ultrafast photoluminescence measurement. ZnO_full_time.pxp file (Igor Pro by WaveMetrics) is the ultrafast photoluminescence(PL) data obtained upon pumping at 4.66eV at different fluence :0.8, 2.3, 4.7, 5.4 (unit: mJ*cm-2), which are plotted in Figure 1, Figure 2, Figure S3, Figure S4, and Figure S5. time_0312cuts.txt, zno1812corr_wn_233nm.txt, PL_cut_fit2exp.ipynb are the time delay, time trace cut at PL peak, and python code for exponential fitting, respectively, which is plotted in Figure S7. ZnO Raman_rise.pxp contains the Raman data of water in comparison of rising of ZnO PL data, which is plotted in Figure S6. Folder "2 - ZnO_XANES_transient": This folder contains the raw data of Zn K-edge fs-XANES transients data measured in SACLA (2ps), which are plotted in Figure 3 and Figure 4. In the SACLA_APS_comparison_XANES.txt file: the headers "allDiff_N", "allscaler1S3_N", "fluonorm_diff_norm", indicates the transient data measured in APS (100ps), ground-state XAS, and the transient fs-XANES data measured in SACLA(2ps), repectively. Folder "3 - ZnO_XANES_timescan":This folder contains the fs-XANES time scan along with the fitting python code, which is plotted in Figure 5 and Figure S8. Folder "4 - ZnO_MD_Simulation" - This folder contains the result of ab-initio molecular dynamics simulations, which is plotted in Figure 6.

We present a simulation model that can be used to study the movement of an object in an acoustic levitator. The model uses the boundary element method (BEM) to compute the levitator's acoustic field, and the finite element method (FEM) to compute the movement of the levitating object. The model was built to act as a virtual tool for testing how objects move in acoustic pressure fields generated by phased array transducers (PATs). This was demonstrated by comparing object dynamics for different PAT optimization methods. We studied the stability of the levitation in fields created by two optimization methods. The fields were optimized to levitate an ellipsoid in the middle of our PAT geometry. By slightly displacing the levitating object from the intended levitation spot, we were able to show that the levitation became unstable and that the object would drop out from the trap. The results demonstrate that the model can be used to rapidly validate optimizers instead of having to run long experiments.

Controlled levitation has been limited to sub-wavelength samples with few exceptions of spheres and cubes. We demonstrate controlled airborne levitation of 2λ-sized complex-shaped objects (3D letters, X, U, T). The pressure field was tailored to a particular sample shape by arranging multiple small asymmetric acoustic traps. This allows stable locking of the object and control of its orientation and position in mid-air. The pressure field was produced with a 450-element phased array (f = 40kHz) featuring element-wise amplitude and phase control.

Ultrasonic guided waves (UGWs) can propagate over long distances with little attenuation and are suitable for non-destructive evaluation of a large area. However, localizing fouling inside the closed systems (e.g., pipes, engines) remains to be challenging. Here we propose to use UGWs that propagate along the pipe circumference along multiple helical paths, thus, providing denser structural information without increasing the number of sensors. When the path crosses fouled area the UGWs propagation properties change, generating unique features into each helical wave. Such aggregate information together with the known structural geometry are then used to fit a latent variable Gaussian process to recover the fouling distribution map in a probabilistic manner.

Localizing fouling in industrial pipes by AI-enhanced ultrasonic means shows promising results. However, the existing methods require large quantities of data measured from multiple locations and thus are either costly due to the number of transducers required or are hampered by readout uncertainties due to reattachment of the sensors. We present a wireless and affordable network of custom-built clamp-on transducers with drive electronics, in which transducers can be used as both transmitters and receivers. The network was used to generate and characterize propagating waves in pipes with and without fouling. The transducers and their driving electronics generate signals with sufficient signal-to-noise ratio in a repeatable manner and are sensitive enough to detect the signal change caused by fouling on a steel pipe.

Advances in phased array of transducers (PATs) allow for precise control of each element's phase and amplitude and can consist of hundreds of transducers. The widely adopted Gerchberg-Saxton type of algorithms do not provide sufficient accuracy for complex control tasks like formation of detailed holograms as they perform well only for a small number of control points with respect to the number of transducers. Here we present an efficient PATs amplitude and phase solver based on the iterative first-order gradient optimization of the user defined loss function (APGO). We demonstrate high resolution hologram reconstruction when the number of control points is 25 times the number of transducers.