Time-resolved Imaging Research Articles

The Value of Different Experimental Observables: A Transient Absorption Study of the Ultraviolet Excitation Dynamics Operating in Nitrobenzene.

Excess energy redistribution dynamics operating in nitrobenzene under hexane and isopropanol solvation were investigated using ultrafast transient absorption spectroscopy (TAS) with a 267 nm pump and a 340-750 nm white light continuum probe. The use of a nonpolar hexane solvent provides a proxy to the gas-phase environment, and the findings are directly compared with a recent time-resolved photoelectron imaging (TRPEI) study on nitrobenzene using the same excitation wavelength [L. Saalbach et al., J. Phys. Chem. A 2021, 125, 7174-7184]. Of note is the observation of a 1/e lifetime of 3.5-6.7 ps in the TAS data that was absent in the TRPEI measurements. This is interpreted as a dynamical signature of the T2 state in nitrobenzene─analogous to observations in the related nitronaphthalene system, and additionally supported by previous quantum chemistry calculations. The discrepancy between the TAS and TRPEI measurements is discussed, with the overall findings providing an example of how different spectroscopic techniques can exhibit varying sensitivity to specific steps along the overall reaction coordinate connecting reactants to photoproducts.

Fluorescence molecular tomography for quantum yield and lifetime.

Fluorescence molecular tomography (FMT) is a promising modality for noninvasive imaging of internal fluorescence agents in biological tissues, especially in small animal models, with applications in diagnosis, therapy, and drug design. In this paper, we present a fluorescent reconstruction algorithm that combines time-resolved fluorescence imaging data with photon-counting microcomputed tomography (PCMCT) images to estimate the quantum yield and lifetime of fluorescent markers in a mouse model. By incorporating PCMCT images, a permissible region of interest of fluorescence yield and lifetime can be roughly estimated as prior knowledge, reducing the number of unknown variables in the inverse problem and improving the image reconstruction stability. Our numerical experiments demonstrate the accuracy and stability of the proposed reconstruction method in the presence of data noise, achieving a reconstruction error of 0.02ns for the fluorescence lifetime and an average relative error of 18% for quantum yield reconstruction.

Rydberg state dynamics and fragmentation mechanism of N,N,N',N'-tetramethylmethylenediamine.

The non-adiabatic relaxation processes and the fragmentation dynamics of Rydberg-excited N,N,N',N'-tetramethylmethylenediamine (TMMDA) are investigated using femtosecond time-resolved photoelectron imaging and time-resolved mass spectroscopy. Excitation at 208nm populates TMMDA in a charge-localized 3p state. Rapid internal conversion (IC) to 3s produces two charge-delocalized conformers with independent time constants and distinct population ratios. As the system explores the 3s potential surface, the structural evolution continues on a 1.55ps timescale, followed by a slower (12.1ps) relaxation to the ground state. A thorough comparison of the time-dependent mass and photoelectron spectra suggests that ionization out of the 3p state ends up with the parent ion, the vibrational energy of which is insufficient for the bond cleavage. On the contrary, by virtue of the additional energy acquired by IC from 3p, the internal energy deposited in 3s is available to break the C-N bond, leading to the fragment ion. The fragmentation is found to occur on the ion surface instead of the Rydberg surface.

Dynamics of cavitating tip vortex

The three-dimensional dynamic behaviour of a tip vortex generated by a NACA $66_2$ -415 hydrofoil is investigated under wetted flow and cavitating conditions using time-resolved tomographic particle image velocimetry. Two main cavitation modes are studied, namely the breathing and double-helical modes. The time-averaged flow field consists of a system of two streamwise vortices for all three conditions. The shape of the tip vortex resembles that of the cavitation mode, instead of a circular cylinder. Multi-scale vortical structures are captured in the instantaneous flow field. The surface oscillation of the cavity contributes to the growth of Kelvin–Helmholtz instability over the tip vortex, leading to the onset of hairpin vortices. Stronger spanwise interaction between the tip vortex and flow separation of the hydrofoil is produced by cavitation, further intensifying the perturbation growth. The proper orthogonal decomposition analysis gives insight into the relationship between cavity oscillation and vortex instability. Two major types of unstable modes of the tip vortex are obtained, leading to serpentine centreline displacement and elliptical deformation motion. The selection of the dominant unstable mode is associated with cavity surface oscillation. For the wetted flow condition, the displacement mode dominates the growth of vortex perturbation, while for breathing mode cavitation, the most energetic unstable mode changes into an elliptical deformation pattern, the disturbance energy of which is negligible in the wetted flow condition. Consistency is found between the peak frequency of the deformation mode and the cavity resonance frequency, indicating the contribution of cavity oscillation to the disturbance growth and breakdown of the tip vortex.

A hybrid machine-learning approach for analysis of methane hydrate formation dynamics in porous media with synchrotron CT imaging.

Fast multi-phase processes in methane hydrate bearing samples pose a challenge for quantitative micro-computed tomography study and experiment steering due to complex tomographic data analysis involving time-consuming segmentation procedures. This is because of the sample's multi-scale structure, which changes over time, low contrast between solid and fluid materials, and the large amount of data acquired during dynamic processes. Here, a hybrid approach is proposed for the automatic segmentation of tomographic data from time-resolved imaging of methane gas-hydrate formation in sandy granular media, which includes a deep-learning 3D U-Net model. To prepare a training dataset for the 3D U-Net, a technique to automate data labeling based on sample-specific information about the mineral matrix immobility and occasional fluid movement in pores is proposed. Automatic segmentation allowed for studying properties of the hydrate growth in pores, as well as dynamic processes such as incremental flow and redistribution of pore brine. Results of the quantitative analysis showed that for typical gas-hydrate stability parameters (100 bar methane pressure, 7°C temperature) the rate of formation is slow (less than 1% per hour), after which the surface area of contact between brine and gas increases, resulting in faster formation (2.5% per hour). Hydrate growth reaches the saturation point after 11 h of the experiment. Finally, the efficacy of the proposed segmentation scheme in on-the-fly automatic data analysis and experiment steering with zooming to regions of interest is demonstrated.

Time-resolved photo-magnetic switching of bubble domains in ferrimagnetic garnets

We investigate the single-domain switching of magnetization with a femtosecond laser pulse in Co-doped iron garnet films using time-resolved magneto-optical imaging. This switching regime is achieved with a pump fluence that is two times lower than the fluence required for multi-domain switching. We observed the picoseconds magnetization dynamics of switching in a single bubble domain with a diameter of a few micrometers by tuning the pump laser fluence. This may provide opportunities for cold photo-magnetic recording in magnetic bits approaching the nano-size scale.

Emission characteristics of bulk aerosols excited by externally focused femtosecond filaments.

The bulk aerosol emissions excited by externally focused femtosecond laser filaments are characterized using time-resolved plasma imaging and spectroscopy. Images of N2 and N2+ plasma fluorescence are used to characterize the filament dimensions. Emission profiles from bulk Sr aerosols are studied, showing that several localized emission regions in the filament begin to develop for lower repetition rates and higher pulse energies. Plasma temperature and electron density profiles are determined using particle emissions along the length of short- and long-focused filaments, and results are compared for on-axis and side-collected spectra. The use of on-axis collection enables the sampling of light emitted over the entire length of the filament; however, the necessary back-propagation of light makes on-axis collection susceptible to self-absorption as the optical path is extended through the filament plasma column formed in bulk aerosols.

Transfer-learning is a key ingredient to fast deep learning-based 4D liver MRI reconstruction

Time-resolved volumetric magnetic resonance imaging (4D MRI) could be used to address organ motion in image-guided interventions like tumor ablation. Current 4D reconstruction techniques are unsuitable for most interventional settings because they are limited to specific breathing phases, lack temporal/spatial resolution, and have long prior acquisitions or reconstruction times. Deep learning-based (DL) 4D MRI approaches promise to overcome these shortcomings but are sensitive to domain shift. This work shows that transfer learning (TL) combined with an ensembling strategy can help alleviate this key challenge. We evaluate four approaches: pre-trained models from the source domain, models directly trained from scratch on target domain data, models fine-tuned from a pre-trained model and an ensemble of fine-tuned models. For that the data base was split into 16 source and 4 target domain subjects. Comparing ensemble of fine-tuned models (N = 10) with directly learned models, we report significant improvements (P < 0.001) of the root mean squared error (RMSE) of up to 12% and the mean displacement (MDISP) of up to 17.5%. The smaller the target domain data amount, the larger the effect. This shows that TL + Ens significantly reduces beforehand acquisition time and improves reconstruction quality, rendering it a key component in making 4D MRI clinically feasible for the first time in the context of 4D organ motion models of the liver and beyond.

Using averaged models from 4D ultrasound strain imaging allows to significantly differentiate local wall strains in calcified regions of abdominal aortic aneurysms

Abdominal aortic aneurysms are a degenerative disease of the aorta associated with high mortality. To date, in vivo information to characterize the individual elastic properties of the aneurysm wall in terms of rupture risk is lacking. We have used time-resolved 3D ultrasound strain imaging to calculate spatially resolved in-plane strain distributions characterized by mean and local maximum strains, as well as indices of local variations in strains. Likewise, we here present a method to generate averaged models from multiple segmentations. Strains were then calculated for single segmentations and averaged models. After registration with aneurysm geometries based on CT-A imaging, local strains were divided into two groups with and without calcifications and compared. Geometry comparison from both imaging modalities showed good agreement with a root mean squared error of 1.22 ± 0.15 mm and Hausdorff Distance of 5.45 ± 1.56 mm (mean ± sd, respectively). Using averaged models, circumferential strains in areas with calcifications were 23.2 ± 11.7% (mean ± sd) smaller and significantly distinguishable at the 5% level from areas without calcifications. For single segmentations, this was possible only in 50% of cases. The areas without calcifications showed greater heterogeneity, larger maximum strains, and smaller strain ratios when computed by use of the averaged models. Using these averaged models, reliable conclusions can be made about the local elastic properties of individual aneurysm (and long-term observations of their change), rather than just group comparisons. This is an important prerequisite for clinical application and provides qualitatively new information about the change of an abdominal aortic aneurysm in the course of disease progression compared to the diameter criterion.

Deuterium imaging of the Warburg effect at sub-millimolar concentrations by joint processing of the kinetic and spectral dimensions.

Deuterium metabolic imaging (DMI) is a promising molecular MRI approach, which follows the administration of deuterated substrates and their metabolization. [6,6'-2 H2 ]-glucose for instance is preferentially converted in tumors to [3,3'-2 H2 ]-lactate as a result of the Warburg effect, providing a distinct resonance whose mapping using time-resolved spectroscopic imaging can diagnose cancer. The MR detection of low-concentration metabolites such as lactate, however, is challenging. It has been recently shown that multi-echo balanced steady-state free precession (ME-bSSFP) increases the signal-to-noise ratio (SNR) of these experiments approximately threefold over regular chemical shift imaging; the present study examines how DMI's sensitivity can be increased further by advanced processing methods. Some of these, such as compressed sensing multiplicative denoising and block-matching/3D filtering, can be applied to any spectroscopic/imaging methods. Sensitivity-enhancing approaches were also specifically tailored to ME-bSSFP DMI, by relying on priors related to the resonances' positions and to features of the metabolic kinetics. Two new methods are thus proposed that use these constraints for enhancing the sensitivity of both the spectral images and the metabolic kinetics. The ability of these methods to improve DMI is evidenced in pancreatic cancer studies carried at 15.2 T, where suitable implementations of the proposals imparted eightfold or more SNR improvement over the original ME-bSSFP data, at no informational cost. Comparisons with other propositions in the literature are briefly discussed.

The dynamics, mixing, and thermonuclear burn of compressed foams with varied gas fills

Inertial confinement fusion (ICF) implosions involve highly coupled physics and complex hydrodynamics that are challenging to model computationally. Due to the sensitivity of such implosions to small features, detailed simulations require accurate accounting of the geometry and dimensionality of the initial conditions, including capsule defects and engineering features such as fill tubes used to insert gas into the capsule, yet this is computationally prohibitive. It is therefore difficult to evaluate whether discrepancies between the simulation and experiment arise from inadequate fidelity to the capsule geometry and drive conditions, uncertainties in physical data used by simulations, or inadequate physics. We present results from detailed high-resolution three-dimensional simulations of ICF implosions performed as part of the MARBLE campaign on the National Ignition Facility [Albright et al., Phys. Plasmas 29, 022702 (2022)]. These experiments are foam-filled separated-reactant experiments, where deuterons reside in the foam and tritons reside in the capsule gas fill and deuterium–tritium (DT) fusion reactions only occur in the presence of mixing between these materials. Material mixing in these experiments is primarily seeded by shock interaction with the complex geometry of the foam and gas fill, which induces the Richtmyer–Meshkov instability. We compare results for experiments with two different gas fills (ArT and HT), which lead to significant differences in the hydrodynamic and thermodynamic developments of the materials in the implosion. Our simulation results show generally good agreement with experiments and demonstrate a substantial impact of hydrodynamic flows on measured ion temperatures. The results suggest that viscosity, which was not included in our simulations, is the most important unmodeled physics and qualitatively explains the few discrepancies between the simulation and experiment. The results also suggest that the hydrodynamic treatment of shocks is inadequate to predict the heating and yield produced during shock flash, when the shock converges at the center of the implosion. Alternatively, underestimation of the level of radiative preheat from the shock front could explain many of the differences between the experiment and simulation. Nevertheless, simulations are able to reproduce many experimental observables within the level of experimental reproducibility, including most yields, time-resolved X-ray self-emission images, and an increase in burn-weighted ion temperature and neutron down-scattered ratio in the line of sight that includes a jet seeded by the glue spot that joins capsule hemispheres.

Time-resolved imaging and analysis of the electron beam-induced formation of an open-cage metallo-azafullerene.

The visualization of single-molecule reactions provides crucial insights into chemical processes, and the ability to do so has grown with the advances in high-resolution transmission electron microscopy. There is currently a limited mechanistic understanding of chemical reactions under the electron beam. However, such reactions may enable synthetic methodologies that cannot be accessed by traditional organic chemistry methods. Here we demonstrate the synthetic use of the electron beam, by in-depth single-molecule, atomic-resolution, time-resolved transmission electron microscopy studies, in inducing the formation of a doubly holed fullerene-porphyrin cage structure from a well-defined benzoporphyrin precursor deposited on graphene. Through real-time imaging, we analyse the hybrid's ability to host up to two Pb atoms, and subsequently probe the dynamics of the Pb-Pb binding motif in this exotic metallo-organic cage structure. Through simulation, we conclude that the secondary electrons, which accumulate in the periphery of the irradiated area, can also initiate chemical reactions. Consequently, designing advanced carbon nanostructures by electron-beam lithography will depend on the understanding and limitations of molecular radiation chemistry.

Time-Resolved Single-Particle X-ray Scattering Reveals Electron-Density Gradients As Coherent Plasmonic-Nanoparticle-Oscillation Source.

Dynamics of optically excited plasmonic nanoparticles are presently understood as a series of scattering events involving the initiation of nanoparticle breathing oscillations. According to established models, these are caused by statistical heat transfer from thermalized electrons to the lattice. An additional contribution by hot-electron pressure accounts for phase mismatches between theory and experimental observations. However, direct experimental studies resolving the breathing-oscillation excitation are still missing. We used optical transient-absorption spectroscopy and time-resolved single-particle X-ray diffractive imaging to access the electron system and lattice. The time-resolved single-particle imaging data provided structural information directly on the onset of the breathing oscillation and confirmed the need for an additional excitation mechanism for thermal expansion. We developed a new model that reproduces all of our experimental observations. We identified optically induced electron density gradients as the initial driving source.

Time-resolved SWIR imaging for the assessment of the activity of occlusal caries lesions.

The aim of this study was to develop a clinical SWIR reflectance handpiece to assess the activity of lesions on the occlusal surfaces. The time-resolved reflectivity of 10 active and 10 arrested occlusal caries lesions on extracted teeth was monitored at 1470 nm using a benchtop system and a modified clinical prototype during forced air drying. The presence of a highly mineralized surface layer measured with microcomputed tomography (microCT) was used to indicate lesion activity. Multiple kinetic parameters were extracted from the acquired SWIR time versus intensity dehydration curves and used to assess lesion activity. Three parameters: delay, %Ifin , and rate calculated from the SWIR dehydration curves were significantly different (p < 0.05) between active and arrested lesions. The modified clinical probe was able to completely dehydrate all the active lesion areas in the occlusal pits and fissures in less than 30 s.

Automatic detection of aortic valve pathology using 3-chamber cine MRI

Abstract Funding Acknowledgements Type of funding sources: None. Introduction The assessment of aortic valve pathology using magnetic resonance imaging (MRI) typically relies on blood velocity estimates acquired using phase contrast (PC) MRI. However, abnormalities in blood flow through the aortic valve often manifest by the dephasing of blood signal in gated balanced steady-state free precession (bSSFP) scans (cine MRI) [1]. This suggests cine MRI may be used to identify patients with blood flow abnormalities who would benefit from additional detailed PC-MRI scans. Nevertheless, there are currently no tools to automatically identify abnormalities in blood flow through the aortic root in cine MRI which can be used to improve diagnosis and optimise clinical CMR protocols. Aim To determine whether a classification neural network (NN) can automatically detect aortic valve pathology from 3-chamber cine MRI, without the need for dedicated additional imaging or manual annotation. Data We train and test our approach on a retrospective clinical dataset from three UK hospitals, using single-slice bSSFP 3-chamber cine MRI (cardiac frames: 31 ± 15, spatial resolution: 1.17–1.56 x 1.17–1.56 x 8 mm3) from n = 577 patients. Using information from clinical assessments, each image was labelled as: aortic regurgitation (AR) (13%), aortic stenosis (AS) (11%), mixed valve disease (MVD) (9%), or showing no relevant pathology (67%). 322 randomly selected images were used for training, 81 for validation and 174 for testing. Methods We trained a 3D classification NN (DenseNet201 model [2] with 4 dense blocks which include 3D (i.e., spatiotemporal) convolutional layers; Adam optimisation and a cross-entropy loss function) to classify the time-resolved cine MR images. We used binary labels: "pathology" (corresponding to AR, AS or MVD) or "no pathology". As a pre-processing step, we automatically identified the heart in all cine MRI frames using an in-house algorithm that detected motion in the image across the cardiac cycle, and cropped the images to remove non-cardiac anatomical structures. After the classification, we used gradient-weighted class activation mapping (Grad-CAM) [3] to gain confidence that the classification was most dependent on voxels close to the aortic root. Fig 1 shows an overview of the image processing pipeline. Results Our classification model accurately predicts aortic valve pathology using 3-chamber cine MRI: Fig 2 shows the confusion matrix and the evaluation metrics of the study. The Grad-CAM heatmaps showed the aortic region contributed the most to the classification, as seen in Fig 1. Conclusion Convolutional NNs can identify aortic valve pathology automatically from 3-chamber cine MRI, without the need for manual annotations. This can be used to improve diagnosis and optimise clinical CMR protocols, namely as a triage tool to identify the patients most likely to benefit from additional PC-MRI sequences.

Fluorescent/Phosphorescent Dual-Emissive Probe for Simultaneous and Independent Sensing of Intracellular Hypochlorite and pH via Time-Resolved Luminescence Imaging.

In this work, we present a dual-emissive polymeric probe capable of simultaneous and independent sensing of intracellular hypochlorite and pH. The probe integrates an anthracene-carboxamide-based fluorescent compound with a phosphorescent iridium(III) complex. The fluorescence and phosphorescence spectra are significantly overlapped at 500 to 650 nm, but their lifetimes differ substantially at 14.8 and 1113.6 ns, respectively, thereby allowing for monitoring in different time intervals through time-resolved luminescence analysis. The fluorescence is quenched upon oxidation by hypochlorite, whereas an acidic environment favors protonation of pyridyl groups on the iridium(III) complex, which causes phosphorescence quenching. During hypochlorite sensing, a linear plot of the fluorescence/phosphorescence ratio over hypochlorite concentration was obtained. During pH sensing, the pK a value was calculated to be about 4.6. The utilization of this probe for imaging cellular endogenous hypochlorite and intracellular pH values was demonstrated. The short-lived and long-lived signals were successfully extracted via time-resolved luminescence imaging microscopy, thereby allowing independent, simultaneous, and quantitative analysis of fluorescence and phosphorescence signals and their response toward hypochlorite and pH values. This study offers potential implications for the development of biosensing and bioimaging applications and paves the way for the simultaneous multiplexed detection of physiological and pathological activities within living cells.

Ozone Generation by Surface Dielectric Barrier Discharge

Surface dielectric barrier discharge (SDBD) is used in a variety of different applications; however, the ozone generated in the discharge can be toxic to people in the vicinity. In this paper, we study the SDBD (using generators with smooth-edge, serrated and thin-wire high-voltage electrodes) in terms of ozone generation. The electrical measurements and the time-resolved plasma imaging revealed differences in the discharge current, dissipated power and plasma morphology for the different types of SDBD generators and showed significant suppression of the streamer formation from the thin-wire electrode. We determined the amount of ozone produced by each generator and found that despite the observed differences in discharge between the generators, the ozone production yield and the maximum volumetric concentration of ozone for all three generators is a linear function of only one parameter—the discharge active power. We also found that the ozone production efficiency of 9.66 g/kWh is constant for all three generators. Our results show that SDBD generators can be safely used in the enclosed space if the SDBD discharge operates with relatively low active power (the SDBD generator working with the active power of 1.7 W did not exceed the ozone concentration of 0.1 ppm in the 60 m3 room).

An Insight into Quasi-Two-Dimensional Flow Features Over Turbine Blading From the Works of Jonathan Paul Gostelow

Abstract The flow through the predominantly two-dimensional geometries of cascades of blades is intrinsically three-dimensional and unsteady. Direct Numerical Simulation, Large Eddy Simulations, and time-resolved Particle Image Velocimetry provide access to the full flow physics, relevant to aerodynamic loss and heat management. Such studies build upon earlier insight drawn from quasi-two-dimensional investigations that identified the key areas where progress in understanding was most needed. These areas stretch across the full passage, from the leading edge of the blade to the passage outflow. Streamwise surface vorticity, transition, the calmed region, shock–boundary layer interaction, and vortex shedding are considered in detail, specifically (i) on what gaps in their physical understanding the works of Jonathan Paul Gostelow exposed and (ii) what gaps were present in the two-dimensional computational approaches used to represent these flows in these works. These useful insights are obtained from the geometrically simpler settings of circular cylinders in cross-flow and from flat plate experiments, as well as from cascades of blades. This paper presents an overview of the physical understanding of the flow features that underpins the more recent time-resolved three-dimensional investigations, led by the late Emeritus Professor Jonathan Paul Gostelow. This work celebrates some of Paul Gostelow’s 50 + years of turbomachinery research achievements and develops awareness about their significance toward reaching a more complete knowledge of the flow physics in turbomachinery, using the more recent time-resolved three-dimensional modeling capability of Computational Fluid Dynamics.

Dynamic lung water MRI during exercise stress.

Exercise-induced dyspnea caused by lung water is an early heart failure symptom. Dynamic lung water quantification during exercise is therefore of interest to detect early stage disease. This study developed a time-resolved 3D MRI method to quantify transient lung water dynamics during rest and exercise stress. The method was evaluated in 15 healthy subjects and 2 patients with heart failure imaged in transitions between rest and exercise, and in a porcine model of dynamic extravascular lung water accumulation through mitral regurgitation (n = 5). Time-resolved images were acquired at 0.55T using a continuous 3D stack-of-spirals proton density weighted sequence with 3.5 mm isotropic resolution, and derived using a motion corrected sliding-window reconstruction with 90-s temporal resolution in 20-s increments. A supine MRI-compatible pedal ergometer was used for exercise. Global and regional lung water density (LWD) and percent change in LWD (ΔLWD) were automatically quantified. A ΔLWD increase of 3.3 ± 1.5% was achieved in the animals. Healthy subjects developed a ΔLWD of 7.8 ± 5.0% during moderate exercise, peaked at 16 ± 6.8% during vigorous exercise, and remained unchanged over 10 min at rest (-1.4 ± 3.5%, p = 0.18). Regional LWD were higher posteriorly compared the anterior lungs (rest: 33 ± 3.7% vs 20 ± 3.1%, p < 0.0001; peak exercise: 36 ± 5.5% vs 25 ± 4.6%, p < 0.0001). Accumulation rates were slower in patients than healthy subjects (2.0 ± 0.1%/min vs 2.6 ± 0.9%/min, respectively), whereas LWD were similar at rest (28 ± 10% and 28 ± 2.9%) and peak exercise (ΔLWD 17 ± 10% vs 16 ± 6.8%). Lung water dynamics can be quantified during exercise using continuous 3D MRI and a sliding-window image reconstruction.

The influence of yaw misalignment on turbine power output fluctuations and unsteady aerodynamic loads within wind farms

Systematic wind tunnel experiments were performed to quantify the power output fluctuations and unsteady aerodynamic loads of modeled wind farms with 3 rows and 3 columns across various yaw angles. Time-resolved particle image velocimetry (PIV) was applied to characterize the flow statistics, while the power output and aerodynamic loads on the turbine tower were measured by a data logger and force cell at high temporal resolution. Results showed that the growth of the yaw misalignment angle mitigates the turbine power output fluctuation. However, this can increase the power fluctuations of downstream turbines. Measurements of the aerodynamic loads on the turbine tower revealed that the growth of the yaw angle significantly increased the fatigue loading in the side-force direction across all frequency components. At the same time, such impact was less distinctive for the thrust force. The dominating unsteady aerodynamic loads are always in the direction perpendicular to the rotor surface. Flow statistics demonstrated that yaw misalignment could effectively increase mean wake velocity, and integral time scale and reduce the turbulence intensity. Finally, theoretical models based on the coupling between turbine properties and local incoming flow statistics were derived to reveal the evolution of turbine power fluctuations and unsteady aerodynamic loads in the wake flow across various yaw misalignment.