Thermal Wavelength Measurement of Nanofluid in an Optical-Fiber Thermal Wave Cavity Technique to Determine the Thermal Diffusivity.

The application of optical-fiber thermal wave cavity (OF-TWC) technique was investigated to measure the thermal diffusivity of Ag nanofluids. The thermal diffusivity was obtained by measuring the thermal wavelength of sample in a cavity scan mode. The spherical Ag nanoparticles samples were prepared at various sizes using the microwave method. Applying the thermal wavelength measurement in a flexible OF-TWC technique requires only two experimental data sets. It can be used to estimate thermal diffusivity of a small amount of liquid samples (0.3 ml) in a brief period. UV-Vis spectroscopy and transmission electron microscopy were used to measure the characterization of the Ag nanoparticles. The thermal diffusivity of distilled water, glycerol, and two different types of cooking oil was measured and has an excellent agreement with the reported results in the literature (difference of only 0.3%-2.4%). The nanofluids showed that the highest value of thermal diffusivity was achieved for smaller sized nanoparticles. The results of this method confirmed that the thermal wavelength measurement method using the OF-TWC technique had potential as a tool to measure the thermal diffusivity of nanofluids with different variables such as the size, shape, and concentration of the nanoparticles.

On the fatigue life of dental implants: Numerical and experimental investigation on configuration effect.

Dental implants have seen widespread and successful use in recent years. Given their long-term application and the critical role of geometry in determining fracture and fatigue characteristics, fatigue assessments are of utmost importance for implant systems. In this study, nine dental implant system samples were subjected to testing in accordance with ISO 14801 standards. The tests included static evaluations to assess ultimate loads and fatigue tests conducted under loads of 270 N and 230 N at a frequency of 15 Hz, aimed at identifying fatigue failure locations and fatigue life. Fatigue life predictions and related calculations were carried out using Fe-safe software. The initial model featured a 22° angle for both the fixture and abutment. Subsequently, variations in abutment angles at 21° and 23° were considered while keeping the fixture angle at 22°. In the next phase, the fixture and abutment angles were set as identical, at 21° and 23°. The results unveiled that when the angles of the abutment and fixture matched, stress values decreased, and fatigue life increased. Conversely, models featuring abutment angles of 21° and 23°, with a 22° angle for the fixture, led to a 49.1 % increase in stress and a 36.9 % decrease in fatigue life compared to the primary model. Notably, in the case of the implant with a 23° angle for both abutment and fixture, the fatigue life reached its highest value at 10 million cycles. Conversely, the worst-case scenario was observed in the implant with a 21° abutment angle and a 23° fixture angle, with a fatigue life of 5.49 million cycles.

Coronary artery lipid accumulation prevention through vibrating piezo electric nano plates embedded in smart stent.

Considering the lipid concentration and side effects regarding the stents used by surgeons, a new heart stent model is proposed. In the new stent, a few piezo plates are designed and attached to the stents by which release of the lipids can take place due to the applied alternative voltages. Due to the vibrations of small-scale piezoelectric plates, the deposition of low-density-lipoproteins (LDL) floating in the blood flow in the coronary arteries is prevented. Small-scale effects are considered using nonlocal elasticity theory, and the interaction between fluid and solid is modeled using the Navier-Stokes equation. The effect of fluid parameters as well as applied voltage and geometry structure is reported. Developing of smart stents maybe the key for prevention of short time conventional stents failure.

Experimental and numerical study on micro-blasting process of 3A dental implant titanium alloy: A comparison between finite element method and smoothed particle hydrodynamics.

In the present study, solid particle erosion due to micro-blasting of dental implants (3A) made of titanium alloy under the impact of multiple alumina particles with an average diameter of 85 µm was analyzed, experimentally and numerically. The numerical investigation was conducted using finite element (FE) and smoothed particle hydrodynamics (SPH) methods. The erosive behavior of this alloy was simulated as impacts in micro-scale based on Johnson-Cook constitutive equations. By focusing on the particles impacts, a representative volume element (RVE) technique was proposed to simulate the arbitrary multiple particle impacts. The results of FE and SPH models are validated and compared with the experimental results. The effects of particle velocity and impact angle on the erosion rate of the alloy are then investigated. Finally, an equation is presented for prediction of the erosion rate versus velocity and angle of impact. The results indicate that for all impact velocities, the combination of penetration and cutting can create a critical condition of erosion damage for the titanium alloy.

Global sensitivity analysis for assessing the parameters importance and setting a stopping criterion in a biomedical inverse problem.

This paper shows how to obtain in addition to the standard deviations available after a data assimilation procedure based on the ensemble Kalman filter, an apportioning of the total uncertainty in the outputs of a patient-specific blood flow model into small portions of uncertainty due to input parameters. Statistical indicators generally used for identifying the importance of numerical parameters, namely the Sobol' first order and total indices, are introduced and discussed. These allow the identification of the importance rank of the different input parameters for the patient-specific blood flow model, as well as the influence of the interactions between these parameters on the model output variance. The results show that knowing the importance rank of the model input parameters during the assimilation procedure is useful to avoid unnecessary over-solving and to find a suitable stopping criterion in clinical situations where faster diagnosis is always requested. Indeed, the work permits to reduce typically by a factor of six the time to solution and most importantly with very limited extra calculation using already available information.

Augmented patient-specific functional medical imaging by implicit manifold learning.

This paper uses machine learning to enrich magnetic resonance angiography and magnetic resonance imaging acquisitions. A convolutional neural network is built and trained over a synthetic database linking geometrical parameters and mechanical characteristics of the arteries to blood flow rates and pressures in an arterial network. Once properly trained, the resulting neural network can be used in order to predict blood pressure in cerebral arteries noninvasively in nearly real-time. One challenge here is that not all input variables present in the synthetic database are known from patient-specific medical data. To overcome this challenge, a learning technique, which we refer to as implicit manifold learning, is employed: in this view, the input and output data of the neural network are selected based on their availability from medical measurements rather than being defined from the mechanical description of the arterial system. The results show the potential of the method and that machine learning is an alternative to costly ensemble based inversion involving sophisticated fluid structure models.

Backward sensitivity analysis and reduced-order covariance estimation in noninvasive parameter identification for cerebral arteries.

Using a previously developed inversion platform for functional cerebral medical imaging with ensemble Kalman filters, this work analyzes the sensitivity of the results with respect to different parameters entering the physical model and inversion procedure, such as the inlet flow rate from the heart, the choice of the boundary conditions, and the nonsymmetry in the network terminations. It also proposes an alternative low complexity construction for the covariance matrix of the hemodynamic parameters of a network of arteries including the circle of Willis. The platform takes as input patient-specific blood flow rates extracted from magnetic resonance angiography and magnetic resonance imaging (dicom files) and is applied to several available patients data. The paper presents full analysis of the results for one of these patients, including a sensitivity study with respect to the proximal and distal boundary conditions. The results notably show that the uncertainties on the inlet flow rate led to uncertainties of the same order of magnitude in the estimated parameters (blood pressure and elastic parameters) and that three-lumped parameters boundary conditions are necessary for a correct retrieval of the target signals.

Photothermal Effect of Modulating Laser Irradiation on the Thermal Diffusivity of Al2O3 Nanofluids.

Modulated continuous wave (CW) lasers cause photothermal effect that leads to rapid optical absorption and generation of thermal waves around the irradiated nanostructures. In this work, we examined the effect of modulated CW laser irradiation on the particle fragmentation process to enhance the thermal diffusivity of nanofluids. A facile and cost-effective diode laser was applied to reduce the agglomerated size of Al2O3 nanoparticles in deionized water. The thermal wave generation, which was determined by the modulated frequency of the laser beam and the optical and thermal properties of the nanofluid, is also briefly discussed and summarized. The influence of laser irradiation time on nanoparticle sizes and their size distribution was determined by dynamic light scattering and transmission electron microscopy. The thermal diffusivity of the nanofluid was measured using the photopyroelectric method. The data obtained showed that the modulated laser irradiation caused the partial fragmentation of some agglomerated particles in the colloids, with an average diameter close to the original particle size, as indicated by a narrow distribution size. The reduction in the agglomerated size of the particles also resulted in an enhancement of the thermal diffusivity values, from 1.444 × 10-3 to 1.498 × 10-3 cm2/s in 0 to 30 min of irradiation time. This work brings new possibilities and insight into the fragmentation of agglomerated nanomaterials based on the photothermal study.

Exact and approximate Fourier rebinning algorithms for the solution of the data truncation problem in 3-D PET.

This paper presents an extended 3-D exact rebinning formula in the Fourier space that leads to an iterative reprojection algorithm (iterative FOREPROJ), which enables the estimation of unmeasured oblique projection data on the basis of the whole set of measured data. In first approximation, this analytical formula also leads to an extended Fourier rebinning equation that is the basis for an approximate reprojection algorithm (extended FORE). These algorithms were evaluated on numerically simulated 3-D positron emission tomography (PET) data for the solution of the truncation problem, i.e., the estimation of the missing portions in the oblique projection data, before the application of algorithms that require complete projection data such as some rebinning methods (FOREX) or 3-D reconstruction algorithms (3DRP or direct Fourier methods). By taking advantage of all the 3-D data statistics, the iterative FOREPROJ reprojection provides a reliable alternative to the classical FOREPROJ method, which only exploits the low-statistics nonoblique data. It significantly improves the quality of the external reconstructed slices without loss of spatial resolution. As for the approximate extended FORE algorithm, it clearly exhibits limitations due to axial interpolations, but will require clinical studies with more realistic measured data in order to decide on its pertinence.

Data assimilation for identification of cardiovascular network characteristics.

A method to estimate the hemodynamics parameters of a network of vessels using an Ensemble Kalman filter is presented. The elastic moduli (Young's modulus) of blood vessels and the terminal boundary parameters are estimated as the solution of an inverse problem. Two synthetic test cases and a configuration where experimental data are available are presented. The sensitivity analysis confirms that the proposed method is quite robust even with a few numbers of observations. The simulations with the estimated parameters recovers target pressure or flow rate waveforms at given specific locations, improving the state-of-the-art predictions available in the literature. This shows the effectiveness and efficiency of both the parameter estimation algorithm and the blood flow model.

Optimization of a microfluidic mixer for studying protein folding kinetics.

We have applied an optimization method in conjunction with numerical simulations to minimize the mixing time of a microfluidic mixer developed for protein folding studies. The optimization method uses a semideterministic algorithm to find the global minimum of the mixing time by varying the mixer geometry and flow conditions. We describe the minimization problem and constraints and give a brief overview of the optimization algorithm. We present results of the optimization, including the optimized geometry and parameter sensitivities, and we demonstrate the improvement in mixing performance with experiments using microfabricated mixers. The dye-quenching experiments of the original and optimized mixer designs show respective mixing times of 7 and 4 mus, a 40% reduction. The new design also provides more uniform mixing across streamlines that enter the mixer. The optimized mixer is the fastest reported continuous flow mixer for protein folding.

Design and optimization of on-chip capillary electrophoresis.

We present a systematic, experimentally validated method of designing electrokinetic injections for on-chip capillary electrophoresis applications. This method can be used to predict point-wise and charge-coupled device (CCD)-imaged electropherograms using estimates of species mobilities, diffusivities and initial sample plug parameters. A simple Taylor dispersion model is used to characterize electrophoretic separations in terms of resolution and signal-to-noise ratio (SNR). Detection convolutions using Gaussian and Boxcar detector response functions are used to relate optimal conditions for resolution and signal as a function of relevant system parameters including electroosmotic mobility, sample injection length, detector length scale, and the length-to-detector. Analytical solutions show a tradeoff between signal-to-noise ratio and resolution with respect to dimensionless injection width and length to the detector. In contrast, there is no tradeoff with respect to the Peclet number as increases in Peclet number favor both SNR and separation solution (R). We validate our model with quantitative epifluorescence visualizations of electrophoretic separation experiments in a simple cross channel microchip. For the pure advection regime of dispersion, we use numerical simulations of the transient convective diffusion processes associated with electrokinetics together with an optimization algorithm to design a voltage control scheme which produces an injection plug that has minimal advective dispersion. We also validate this optimal injection scheme using fluorescence visualizations. These validations show that optimized voltage scheme produces injections with a standard deviation less than one-fifth of the width of the microchannel.