Rapid, efficient and reproducible drillcore logging is fundamental in mineral exploration. Drillcore mapping has evolved rapidly in the recent decade, especially with the advances in hyperspectral spectral imaging. A wide range of imaging sensors is now available, providing rapidly increasing spectral as well as spatial resolution and coverage. However, the fusion of data acquired with multiple sensors is challenging and usually not conducted operationally. We propose an innovative solution based on the recent developments made in machine learning to integrate such multi-sensor datasets. Image feature extraction using orthogonal total variation component analysis enables a strong reduction in dimensionality and memory size of each input dataset, while maintaining the majority of its spatial and spectral information. This is in particular advantageous for sensors with very high spatial and/or spectral resolution, which are otherwise difficult to jointly process due to their large data memory requirements during classification. The extracted features are not only bound to absorption features but recognize specific and relevant spatial or spectral patterns. We exemplify the workflow with data acquired with five commercially available hyperspectral sensors and a pair of RGB cameras. The robust and efficient spectral-spatial procedure is evaluated on a representative set of geological samples. We validate the process with independent and detailed mineralogical and spectral data. The suggested workflow provides a versatile solution for the integration of multi-source hyperspectral data in a diversity of geological applications. In this study, we show a straight-forward integration of visible/near-infrared (VNIR), short-wave infrared (SWIR) and long-wave infrared (LWIR) data for sensors with highly different spatial and spectral resolution that greatly improves drillcore mapping.

New energy, transport, computer and telecommunication technologies require an increasing supply of rare earth elements (REEs). As a consequence, adequate and robust detection methods become essential for the exploration and discovery of new deposits, the improved characterization of existing deposits and the future recycling of today’s high-tech products. Within this paper, we investigate the potential of combining passive reflectance (imaging and point sampling) with laser stimulated luminescence (point sampling) spectroscopic measurements across the visible, near and shortwave infrared for REE detection in non-invasive near-field mineral exploration. We analyse natural REE-bearing mineral samples from main REE-deposits around the world and focus on challenges such as the discrimination of overlapping spectroscopic features and the influence of the mineral type on detectability, feature position and mineral matrix luminescence. We demonstrate that the cross-validation of results from both methods increases the robustness and sensitivity, provides the potential for semi-quantification and enables the time- and cost-efficient detection of economically important REE, including Ce, Pr, Nd, Sm, Eu, Dy, Er, Yb and potentially also Ho and Tm.

Electronic waste is the fastest growing type of scrap globally and is an important challenge due to its heterogeneity, intrinsic toxicity and potential environmental impact. With an objective of obtaining information on the composition of printed circuit boards (PCBs) through non-invasive analysis to aid in recycling and recovery of precious waste, the goal of this paper is to propose a scheme towards the fusion of RGB and hyperspectral data in object detection. State-of-art detectors come with their own set of challenges which make them inapplicable to PCB recycling. We introduce a method which promises to achieve object detection based on multi-sensor data by utilizing the hyperspectral data to localize components and compare the results to a conventional single-sensor (RGB) based approach.

Ge-nanocrystals (NCs) were synthesized in amorphous TaZrO_x by thermal annealing of co-sputtered Ge-TaZrO_x layers. Formation of spherical shaped Ge-NCs with small variation of size, areal density, and depth distribution was confirmed by high-resolution transmission electron microscopy. The charge storage characteristics of the Ge-NCs were investigated by capacitance-voltage and constant-capacity measurements using metal-insulator-semiconductor structures. Samples with Ge-NCs exhibit a maximum memory window of 5 V by sweeping the bias voltage from −7 V to 7 V and back. Below this maximum, the width of the memory window can be controlled by the bias voltage. The fitted slope of the memory window versus bias voltage characteristics is very close to 1 for samples with one layer Ge-NCs. A second layer Ge-NCs does not result in a second flat stair in the memory window characteristics. Constant-capacity measurements indicate charge storage in trapping centers at the interfaces between the Ge-NCs and the surrounding materials (amorphous matrix/tunneling oxide). Charge loss occurs by thermal detrapping and subsequent band-to-band tunneling. Reference samples without Ge-NCs do not show any memory window.

Terephthalic acid on metal surfaces is a system of high interest for interfacial layers as well as for use in generating surface nanostructures by supramolecular self-assembly. Here we demonstrate structural transitions upon compression of the first layer as well as a significant transition from a flat lying chemisorbed first monolayer to a standing second layer. The second layer structure is stable at 150 °C, but a weakly bound state is observed to desorb molecularly below that temperature, likely a transient mobile state during annealing. Molecular resolution scanning tunnelling microscopy is complemented by X-ray photoelectron spectroscopy and thermal desorption spectroscopy in this study. These results provide general insight in the development of self-assembled organic thin films at surfaces, especially with regard to the nature of the metal/organic interface and growth transitions to maximize first layer packing and obtain a second layer that bridges commensurability with the substrate and a more bulk-like structure.

Better quality control for alloy manufacturing and sorting of post-consumer scraps relies heavily on the accurate determination of their chemical composition. In recent decades, analytical techniques, such as X-ray fluorescence spectroscopy (XRF), laser-induced breakdown spectroscopy (LIBS), and spark optical emission spectroscopy (spark-OES), found widespread use in the metal industry, though only a few studies were published about the comparison of these techniques for commercially available alloys. Hence, we conducted a study on the evaluation of four analytical techniques (energy-dispersive XRF, wavelength-dispersive XRF, LIBS, and spark-OES) for the determination of metal sample composition. It focuses on the quantitative analysis of nine commercial alloys, representing the three most important alloy classes: copper, aluminum, and steel. First, spark-OES is proven to serve as a validation technique in the use of certified alloy reference samples. Following an examination of the lateral homogeneity by XRF, the results of the techniques are compared, and reasons for deviations are discussed. Finally, a more general evaluation of each technique with its capabilities and limitations is given, taking operation-relevant parameters, such as measurement speed and calibration effort, into account. This study shall serve as a guide for the routine use of these methods in metal producing and recycling industries.