3D-based reconstruction using growing neural gas landmark: application to rapid prototyping in shoe last manufacturing

Customizing shoe manufacturing is one of the great challenges in the footwear industry. It is a production model change where design adopts not only the main role, but also the main bottleneck. It is therefore necessary to accelerate this process by improving the accuracy of current methods. Rapid prototyping techniques are based on the reuse of manufactured footwear lasts so that they can be modified with CAD systems leading rapidly to new shoe models. In this work, we present a shoe last fast reconstruction method that fits current design and manufacturing processes. The method is based on the scanning of shoe last obtaining sections and establishing a fixed number of landmarks onto those sections to reconstruct the shoe last 3D surface. Automated landmark extraction is accomplished through the use of the self-organizing network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates up to 12 times the surface reconstruction and filtering processes used by the current shoe last design software. The proposed method offers higher accuracy compared with methods with similar efficiency as voxel grid.

Thermogravimetry and Py–GC/MS techniques as fast qualitative methods for comparing the biochemical composition of Nannochloropsis oculata samples obtained under different culture conditions

Microalgae have many applications, such as biodiesel production or food supplement. Depending on the application, the optimization of certain fractions of the biochemical composition (proteins, carbohydrates and lipids) is required. Therefore, samples obtained in different culture conditions must be analyzed in order to compare the content of such fractions. Nevertheless, traditional methods necessitate lengthy analytical procedures with prolonged sample turn-around times. Results of the biochemical composition of Nannochloropsis oculata samples with different protein, carbohydrate and lipid contents obtained by conventional analytical methods have been compared to those obtained by thermogravimetry (TGA) and a Pyroprobe device connected to a gas chromatograph with mass spectrometer detector (Py–GC/MS), showing a clear correlation. These results suggest a potential applicability of these techniques as fast and easy methods to qualitatively compare the biochemical composition of microalgal samples.

Ordered mesoporous titanium oxide for thin film microbatteries with enhanced lithium storage

A 3D mesoporous TiO2 material with well-developed mesostructure is prepared in the form of a binder-free thin (100 nm) film and studied as potential candidate for the negative electrode in lithium microbatteries. By appropriate thermal treatments, the selected crystal structure (anatase, rutile, or amorphous), and micro-/mesostructure of the materials was obtained. The effects of voltage window and prelithiation treatment improved first cycle reversibility up to 86% and capacity retention of 90% over 100 cycles. After a prolonged intercalation of lithium ions in ordered mesoporous TiO2 appeared small particles assigned to Li2Ti2O4 with cubic structure as observed from ex-situ TEM micrographs. This study highlights the flexibility of the potential window to which the electrode can operate. Maximum capacity values over 100 cycles of 470 μA h cm−2 μm−1 and 177 μA h cm−2 μm−1 are obtained for voltage ranges of 0.1–2.6 V and 1.0–2.6 V, respectively. The observed values are between 6 and 2 times higher than those obtained for films with 600 nm (80 μA h cm−2 μm−1) and 900 nm (92 μA h cm−2 μm−1) lengths. This indicates that 100 nm thin TiO2 films with high accessibility show finite-length type diffusion which is interesting for this particular application.