Poly(lactide)/cellulose nanocrystal nanocomposites by high-shear mixing

There is currently considerable interest in developing stiff, strong, tough, and heat resistant poly(lactide) (PLA) based materials with improved melt elasticity in response to the increasing demand for sustainable plastics. However, simultaneous optimization of stiffness, strength, and toughness is a challenge for any material, and commercial PLA is well-known to be inherently brittle and temperature-sensitive and to show poor melt elasticity. In this study, we report that high-shear mixing with cellulose nanocrystals (CNC) leads to significant improvements in the toughness, heat resistance, and melt elasticity of PLA while further enhancing its already outstanding room temperature stiffness and strength. This is evidenced by (i) one-fold increase in the elastic modulus (6.48 GPa), (ii) 43% increase in the tensile strength (87.1 MPa), (iii) one-fold increase in the strain at break (∼6%), (iv) two-fold increase in the impact strength (44.2 kJ/m²), (v) 113-fold increase in the storage modulus at 90°C (787.8 MPa), and (vi) 10³-fold increase in the melt elasticity at 190°C and 1 rad/s (∼10⁵ Pa) via the addition of 30 wt% CNC. It is hence possible to produce industrially viable, stiff, strong, tough, and heat resistant green materials with improved melt elasticity through high-shear mixing.     

Magnetic field enhancement of electrochemical hydrogen evolution reaction probed by magneto-optics

External magnetic fields affect various electrochemical processes and can be used to enhance the efficiency of the electrochemical water splitting reaction. However, the driving forces behind this effect are poorly understood due to the analytical challenges of the available interface-sensitive techniques. Here, we present a set-up based on magneto- and electro-optical probing, which allows to juxtapose the magnetic properties of the electrode with the electrochemical current densities in situ at various applied potentials and magnetic fields. On the example of an archetypal hydrogen evolution catalyst, Pt (in a form of Co/Pt superlattice), we provide evidence that a magnetic field acts on the electrochemical double layer affecting the local concentration gradient of hydroxide ions, which simultaneously affects the magneto-optical and magnetocurrent response.     

Estimating crown diameters in urban forests with Unmanned Aerial System-based photogrammetric point clouds

Field measurements are the main source of information when determining stand parameters, which are essential to produce an effective forest management plan. However, conducting terrestrial measurements is neither time- nor cost-efficient in most cases. In recent years, the advent of sophisticated remote sensing technologies has enabled the extraction of accurate and robust information about the physical characteristics of trees. Crown diameter is one of the most important stand parameters that should be measured or estimated. This study proposes a Polynomial Fitting Based (PFB) methodology to estimate crown diameters of urban trees with Unmanned Aerial System (UAS)-based data. Crown diameters estimated with the PFB methodology were compared not only to a reference data but also to those estimated based on five widely used image segmentation algorithms, which were the Mean Shift Segmentation (MSS), Morphological Profiles Based Segmentation (MPBS), Multiresolution Segmentation (MRS), Seeded Region Growing Segmentation (SRGS) and Watershed Segmentation (WS). Quantitative investigations revealed that the PFB approach outperformed the other segmentation-based approaches. The PFB approach estimated the crown diameters with root-mean-square errors (RMSE) ranging from 0.69 m to 0.92 m. The PFB methodology was found to be a practical and robust approach for the estimation of crown diameters, which plays a very significant role in effective forest management.     

Improved prediction methods for scalable predictive animated mesh compression

Animated meshes represented as sequences of static meshes sharing the same connectivity require efficient compression. Among the compression techniques, layered predictive coding methods efficiently encode the animated meshes in a structured way such that the successive reconstruction with an adaptable quality can be performed. The decoding quality heavily depends on how well the prediction is performed in the encoder. Due to this fact, in this paper, three novel prediction structures are proposed and integrated into a state of the art layered predictive coder. The proposed structures are based on weighted spatial prediction with its weighted refinement and angular relations of triangles between current and previous frames. The experimental results show that compared to the state of the art scalable predictive coder, up to 30% bitrate reductions can be achieved with the combination of proposed prediction schemes depending on the content and quantization level.     

All‐pass filters using DDCC‐ and MOSFET‐based electronic resistor

The main motivation in this paper is to draw attention to the tunability and input‐signal amplitude limitations when a nonlinear device is used as a resistor. For this purpose, two first‐order all‐pass filters are proposed using differential difference current conveyor (DDCC), a capacitor and a resistor without element‐matching restriction. These all‐pass filter circuits can be made electronically tunable with electronic resistors. Tunability and input‐signal amplitude limitations of the proposed circuits due to the operational restrictions of the electronic resistors are examined. PSPICE simulations confirm the validity and the practical utility of the proposed circuits. Copyright © 2010 John Wiley & Sons, Ltd.