@article{hai_shao_ware_jones_sun_2023, title={3D Printing a Low-Cost Miniature Accommodating Optical Microscope}, ISSN={["1521-4095"]}, DOI={10.1002/adma.202208365}, abstractNote={Abstract}, journal={ADVANCED MATERIALS}, author={Hai, Rihan and Shao, Guangbin and Ware, Henry Oliver T. and Jones, Evan Hunter and Sun, Cheng}, year={2023}, month={Jan} }
 @article{dong_cole_onuorah_ware_muller_2023, title={Random matrix theory (RMT) to quantify scattering behavior in lung mimicking phantoms}, volume={153}, ISSN={["1520-8524"]}, DOI={10.1121/10.0018611}, abstractNote={As we previously reported in rodent lungs, RMT parameters (Expected value E(x), and λmax, the eigenvalue with the highest probability) extracted from singular value decomposition (SVD) of inter-element response matrix (IRM) show a significant correlation with fibrosis histology scores. The lack of fibrotic models for larger lungs such as pigs motivated us to investigate porous 3D printed PEGDA phantoms with controllable strut size and alveolar density. We hypothesize that E(x), and λmax can distinguish phantoms with different alveolar size, by evaluating multiple scattering in the backscattered signals. IRMs were acquired using a 128-element linear array L7-4 (Verasonics, at 5.2 MHz central frequency) connected to a Verasonics Vantage ultrasound scanner. Phantoms of 1-inches size with different strut diameters of 0.085, 0.17, and 0.26 mm were used. Attenuation constants for these samples were measured using the Substitution Method at 1 MHz, 2.25MHz, and 5 MHz to evaluate scattering attenuation. E(x), and λmax were evaluated using SVD of the IRM. Attenuation constants, E(x), and λmax all show that the phantoms with larger strut size exhibit more multiple scattering than phantoms with smaller strut size. These preliminary results suggest that such phantoms could be used to mimic pulmonary fibrosis.}, number={3}, journal={JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA}, author={Dong, Zihan and Cole, Azadeh D. and Onuorah, Chukwuka W. and Ware, Henry O. and Muller, Marie}, year={2023}, month={Mar} }