Direct mineralogical imaging of economic ore and rock samples with multi-modal nonlinear optical microscopy

Dual Phase Lock-In Amplifier with Photovoltaic Modules and Quasi-Invariant Common-Mode Signal

In measuring small voltage deviations of about 1 µV and lower, it is important to separate useful signals from noise. The measurement of small voltage deviations between the amplitudes of two AC signals in wide frequency and voltage ranges, is performed by using lock-in amplifiers with the differential input as a comparator (null-indicator). The resolution and measurement accuracy of lock-in amplifiers is largely determined by the common-mode rejection ratio in their measuring channel. This work presents a developed differential signal recovery circuit with embedded photovoltaic modules, which allows implementing the dual phase lock-in amplifier with the differential input and quasi-invariant common-mode signal. The obtained metrological parameters of the proposed dual phase analog lock-in amplifier prove its applicability in comparing two signal amplitudes of 10√2 µV to 10√2 V in the frequency range of 20 Hz to 100 kHz with a 10 nV resolution. The proposed dual phase analog lock-in amplifier was characterized by a 130 to 185 dB CMRR in the frequency range up to 100 kHz with 20 nV/√Hz white noise.

Unsupervised hyperspectral stimulated Raman microscopy image enhancement: denoising and segmentation via one-shot deep learning

Hyperspectral stimulated Raman scattering (SRS) microscopy is a label-free technique for biomedical and mineralogical imaging which can suffer from low signal-to-noise ratios. Here we demonstrate the use of an unsupervised deep learning neural network for rapid and automatic denoising of SRS images: UHRED (Unsupervised Hyperspectral Resolution Enhancement and Denoising). UHRED is capable of "one-shot" learning; only one hyperspectral image is needed, with no requirements for training on previously labelled datasets or images. Furthermore, by applying a k-means clustering algorithm to the processed data, we demonstrate automatic, unsupervised image segmentation, yielding, without prior knowledge of the sample, intuitive chemical species maps, as shown here for a lithium ore sample.

Background-suppressed SRS fingerprint imaging with a fully integrated system using a single optical parametric oscillator

Stimulated Raman Scattering (SRS) imaging can be hampered by non-resonant parasitic signals that lead to imaging artifacts and eventually overwhelm the Raman signal of interest. Stimulated Raman gain opposite loss detection (SRGOLD) is a three-beam excitation scheme capable of suppressing this nonlinear background while enhancing the resonant Raman signal. We present here a compact electro-optical system for SRGOLD excitation which conveniently exploits the idler beam generated by an optical parametric oscillator (OPO). We demonstrate its successful application for background suppressed SRS imaging in the fingerprint region. This system constitutes a simple and valuable add-on for standard coherent Raman laser sources since it enables flexible excitation and background suppression in SRS imaging.

Oblique angle transient-reflectivity laser-scanning microscopy for mineral imaging in natural ores

The microscopic arrangement of different minerals in ores is of high interest for mine planning, mineral processing and extractive metallurgy. Many economically important, naturally occurring minerals are highly absorbing semiconductors. To characterize these materials, we have implemented pump-probe laser scanning microscopy (LSM) in a two-lens reflective configuration that offers efficient collection of signal light by using a combination of galvanometer and sample stage scanning. We show that the short-time (∼10 ps) pump-probe response of a material allows us to distinguish economically important sulfide minerals.