Handheld UV fluorescence spectrophotometer device for the classification and analysis of petroleum oil samples.

Oil spills can be environmentally devastating and result in unintended economic and social consequences. An important element of the concerted effort to respond to spills includes the ability to rapidly classify and characterize oil spill samples, preferably on-site. An easy-to-use, handheld sensor is developed and demonstrated in this work, capable of classifying oil spills rapidly on-site. Our device uses the computational power and affordability of a Raspberry Pi microcontroller and a Pi camera, coupled with three ultraviolet light emitting diodes (UV-LEDs), a diffraction grating, and collimation slit, in order to collect a large data set of UV fluorescence fingerprints from various oil samples. Based on a 160-sample (in 5x replicates each with slightly varied dilutions) database this platform is able to classify oil samples into four broad categories: crude oil, heavy fuel oil, light fuel oil, and lubricating oil. The device uses principal component analysis (PCA) to reduce spectral dimensionality (1203 features) and support vector machine (SVM) for classification with 95% accuracy. The device is also able to predict some physiochemical properties, specifically saturate, aromatic, resin, and asphaltene percentages (SARA) based off linear relationships between different principal components (PCs) and the percentages of these residues. Sample preparation for our device is also straightforward and appropriate for field deployment, requiring little more than a Pasteur pipette and not being affected by dilution factors. These properties make our device a valuable field-deployable tool for oil sample analysis.

Label-free Mie Scattering Identification of Tumor Tissue Using an Angular Photodiode Array.

Tumors differ from normal tissues in several meaningful ways including cellular size, morphology, and protein expression, which will accordingly change the refractive index and the size/morphology of cells. There are also important differences in tissue organization and unique tissue specific cell densities. Instead of time-consuming and labor-intensive histology involving the use of a benchtop microscope, a plot of Mie scattering intensities at fixed wavelength against scattering angle, which we referred to as "Mie spectrum," is suggested as an alternative to identify tumor from normal tissues. An angular photodiode array is developed to measure this Mie spectrum with three different light emitting diodes (blue, green and red) as light sources. The resulting Mie spectra show characteristic peaks for rat colonic tissues, and substantial differences can be found between tumor vs. normal tissues. Two peaks were identified at 120° and 150° scattering angles, potentially representing capillaries and colon cells, respectively. Contributions from crypts and goblet cells, represented by the scattering at 140°, were minimal. Substantial differences between tumor and normal tissues were found with 45°-70° light irradiation angles.

Simplified White Blood Cell Differential: An Inexpensive, Smartphone- and Paper-Based Blood Cell Count.

Sorting and measuring blood by cell type is extremely valuable clinically and provides physicians with key information for diagnosing many different disease states including: leukemia, autoimmune disorders, bacterial infections, etc. Despite the value, the present methods are unnecessarily costly and inhibitive particularly in resource poor settings, as they require multiple steps of reagent and/or dye additions and subsequent rinsing followed by manual counting using a hemocytometer, or they require a bulky, expensive equipment such as a flow cytometer. While direct on-paper imaging has been considered challenging, paper substrate offers a strong potential to simplify such reagent/dye addition and rinsing. In this work, three-layer paper-based device is developed to automate such reagent/dye addition and rinsing via capillary action, as well as separating white blood cells (WBCs) from whole blood samples. Direct onpaper imaging is demonstrated using a commercial microscope attachment to a smartphone coupled with a blue LED and 500 nm long pass optical filter. Image analysis is accomplished using an original MATLAB code, to evaluate the total WBC count, as well as differential WBC count, i.e., granulocytes (primarily neutrophils) vs. agranulocytes (primarily lymphocytes). Only a finger-prick of whole blood is required for this assay. The total assay time from finger-prick to data collection is under five minutes. Comparison with a hemocytometry-based manual counting corroborates the accuracy and effectiveness of the proposed method. This approach could be potentially used to help make blood cell counting technologies more readily available, especially in resource poor, point-of-care settings.