Heat Dissipation of Hybrid Iron Oxide-Gold Nanoparticles in an Agar Phantom

Hybrid iron oxide-gold nanoparticles (HNPs) have shown potential in cancer therapy as agents for tumour ablation
and thermal switches for targeted drug release. Heat generation occurs by exploitation of the surface plasmon
resonance of the gold coating, which usually occurs at the maximum UV absorption wavelength. However, lasers
at such wavelength are often expensive and highly specialised. Here, we report the heating and monitoring of heat
dissipation of HNPs suspended in agar phantoms using a relatively inexpensive Ng: YAG pulsed 1064 nm laser source.
The particles experience heating of up to 40°C with a total area of heat dissipation up to 132.73 mm2 from the 1 mm
diameter irradiation point after 60 seconds. This work reports the potential and possible drawbacks of these particles
for translation into cancer therapy based on our findings.

Getting to the heart of cardiac remodeling; how collagen subtypes may contribute to phenotype

The objective of this study was to investigate the nature and biomechanical properties of collagen fibers within the human myocardium. Targeting cardiac interstitial abnormalities will likely become a major focus of future preventative strategies with regard to the management of cardiac dysfunction. Current knowledge regarding the component structures of myocardial collagen networks is limited, further delineation of which will require application of more innovative technologies. We applied a novel methodology involving combined confocal laser scanning and atomic force microscopy to investigate myocardial collagen within ex-vivo right atrial tissue from 10 patients undergoing elective coronary bypass surgery. Immuno-fluorescent co-staining revealed discrete collagen I and III fibers. During single fiber deformation, overall median values of stiffness recorded in collagen III were 37±16% lower than in collagen I [p<0.001]. On fiber retraction, collagen I exhibited greater degrees of elastic recoil [p<0.001; relative percentage increase in elastic recoil 7±3%] and less energy dissipation than collagen III [p<0.001; relative percentage increase in work recovered 7±2%]. In atrial biopsies taken from patients in permanent atrial fibrillation (n=5) versus sinus rhythm (n=5), stiffness of both collagen fiber subtypes was augmented (p<0.008). Myocardial fibrillar collagen fibers organize in a discrete manner and possess distinct biomechanical differences; specifically, collagen I fibers exhibit relatively higher stiffness, contrasting with higher susceptibility to plastic deformation and less energy efficiency on deformation with collagen III fibers. Augmented stiffness of both collagen fiber subtypes in tissue samples from patients with atrial fibrillation compared to those in sinus rhythm are consistent with recent published findings of increased collagen cross-linking in this setting.

Parametric CAD model based shape optimization using adjoint functions

Adjoint methods have proven to be an efficient way of calculating the gradient of an objective function with respect to a shape parameter for optimisation, with a computational cost nearly independent of the number of the design variables [1]. The approach in this paper links the adjoint surface sensitivities (gradient of objective function with respect to the surface movement) with the parametric design velocities (movement of the surface due to a CAD parameter perturbation) in order to compute the gradient of the objective function with respect to CAD variables.
For a successful implementation of shape optimization strategies in practical industrial cases, the choice of design variables or parameterisation scheme used for the model to be optimized plays a vital role. Where the goal is to base the optimization on a CAD model the choices are to use a NURBS geometry generated from CAD modelling software, where the position of the NURBS control points are the optimisation variables [2] or to use the feature based CAD model with all of the construction history to preserve the design intent [3]. The main advantage of using the feature based model is that the optimized model produced can be directly used for the downstream applications including manufacturing and process planning.
This paper presents an approach for optimization based on the feature based CAD model, which uses CAD parameters defining the features in the model geometry as the design variables. In order to capture the CAD surface movement with respect to the change in design variable, the “Parametric Design Velocity” is calculated, which is defined as the movement of the CAD model boundary in the normal direction due to a change in the parameter value.
The approach presented here for calculating the design velocities represents an advancement in terms of capability and robustness of that described by Robinson et al. [3]. The process can be easily integrated to most industrial optimisation workflows and is immune to the topology and labelling issues highlighted by other CAD based optimisation processes. It considers every continuous (“real value”) parameter type as an optimisation variable, and it can be adapted to work with any CAD modelling software, as long as it has an API which provides access to the values of the parameters which control the model shape and allows the model geometry to be exported. To calculate the movement of the boundary the methodology employs finite differences on the shape of the 3D CAD models before and after the parameter perturbation. The implementation procedure includes calculating the geometrical movement along a normal direction between two discrete representations of the original and perturbed geometry respectively. Parametric design velocities can then be directly linked with adjoint surface sensitivities to extract the gradients to use in a gradient-based optimization algorithm.
The optimisation of a flow optimisation problem is presented, in which the power dissipation of the flow in an automotive air duct is to be reduced by changing the parameters of the CAD geometry created in CATIA V5. The flow sensitivities are computed with the continuous adjoint method for a laminar and turbulent flow [4] and are combined with the parametric design velocities to compute the cost function gradients. A line-search algorithm is then used to update the design variables and proceed further with optimisation process.

Improved Real-time State-of-Charge Estimation of LiFePO4 Battery Based on a Novel Thermoelectric Model

Li-ion batteries have been widely used in electric vehicles, and battery internal state estimation plays an important role in the battery management system. However, it is technically challenging, in particular, for the estimation of the battery internal temperature and state-ofcharge (SOC), which are two key state variables affecting the battery performance. In this paper, a novel method is proposed for realtime simultaneous estimation of these two internal states, thus leading to a significantly improved battery model for realtime SOC estimation. To achieve this, a simplified battery thermoelectric model is firstly built, which couples a thermal submodel and an electrical submodel. The interactions between the battery thermal and electrical behaviours are captured, thus offering a comprehensive description of the battery thermal and electrical behaviour. To achieve more accurate internal state estimations, the model is trained by the simulation error minimization method, and model parameters are optimized by a hybrid optimization method combining a meta-heuristic algorithm and the least square approach. Further, timevarying model parameters under different heat dissipation conditions are considered, and a joint extended Kalman filter is used to simultaneously estimate both the battery internal states and time-varying model parameters in realtime. Experimental results based on the testing data of LiFePO4 batteries confirm the efficacy of the proposed method.

Modeling of Turbulent Natural Gas and Biogas Flames of the Delft Jet-in-Hot-Coflow Burner: Effects of Coflow Temperature, Fuel Temperature and Fuel Composition on the Flame Lift-Off Height

The structure of a turbulent non-premixed flame of a biogas fuel in a hot and diluted coflow mimicking moderate and intense low dilution (MILD) combustion is studied numerically. Biogas fuel is obtained by dilution of Dutch natural gas (DNG) with CO2. The results of biogas combustion are compared with those of DNG combustion in the Delft Jet-in-Hot-Coflow (DJHC) burner. New experimental measurements of lift-off height and of velocity and temperature statistics have been made to provide a database for evaluating the capability of numerical methods in predicting the flame structure. Compared to the lift-off height of the DNG flame, addition of 30 % carbon dioxide to the fuel increases the lift-off height by less than 15 %. Numerical simulations are conducted by solving the RANS equations using Reynolds stress model (RSM) as turbulence model in combination with EDC (Eddy Dissipation Concept) and transported probability density function (PDF) as turbulence-chemistry interaction models. The DRM19 reduced mechanism is used as chemical kinetics with the EDC model. A tabulated chemistry model based on the Flamelet Generated Manifold (FGM) is adopted in the PDF method. The table describes a non-adiabatic three stream mixing problem between fuel, coflow and ambient air based on igniting counterflow diffusion flamelets. The results show that the EDC/DRM19 and PDF/FGM models predict the experimentally observed decreasing trend of lift-off height with increase of the coflow temperature. Although more detailed chemistry is used with EDC, the temperature fluctuations at the coflow inlet (approximately 100K) cannot be included resulting in a significant overprediction of the flame temperature. Only the PDF modeling results with temperature fluctuations predict the correct mean temperature profiles of the biogas case and compare well with the experimental temperature distributions.