Fabrication and application of conducting polymer micro- and nano-patterns

Conducting polymers, since their discovery, have attracted significant attention due to their promise of replacing silicon and metals in building devices. They have been shown to have wide applications for biological and chemical sensing as well as for electronic devices. Previous and current efforts have concentrated on building devices of a specific function. When multiple micropatterns of different conducting polymers are fabricated on a common substrate, a versatile microsystem can be envisioned. The existing conducting polymer patterning techniques present some technical challenges of degradation, low throughput, low resolution, depth of field, and residual layer in producing conducting polymer microstructures. To circumvent these challenges in the existing technology, the Intermediate-Layer Lithography (ILL) method is proposed in this study. This approach overcomes the depth of field and residual layer issues of the traditional hot embossing process. Conducting polymer micropatterns of various dimensions have been fabricated. The conducting polymers used for patterning include polypyrrole (PPy), poly(3,4-ethylenedioxythiophen)-poly(4-styrenesulphonate) (PEDOT-PSS), and sulphonated polyaniline (SPANI). Straight and serpentine microwires of various dimensions were fabricated and the embossing recipe was finalized for a stable reproduction of the imprinting results. The fabricated microwires were used for sensing applications using the chemiresistor principle. Sensitivities of conducting polymer films and microwires were compared after they were exposed to different levels of humidity. The microwires were found to be more sensitive than films at lower humidity levels. Two sets of microwires of PPy, PEDOT-PSS, and SPANI were imprinted on a common substrate. The imprinted microwires were used for sensing methanol, toluene, and acetone, individually and in mixtures of two gases. Each of the three different conducting polymer microwires was found to be more sensitive to methanol and acetone compared to toluene. An additional layer of glucose oxidase was coated over the PPy microwires to sense for glucose. The response current of the PPy microwires increased with increasing concentration of glucose (0.2 mg/ml–0.8 mg/ml). The relationship between the surface-to-volume ratio of the microwires and their sensitivities was also investigated. PPy and PEDOT-PSS microwires of various dimensions were fabricated and exposed to acetone vapor at low concentrations. The microwires with higher surface-to-volume ratio were found to be most sensitive at lower concentrations of acetone. PPy nanowires were fabricated effectively using the ILL method. The widths of the wires were 100 nm and 500 nm with lengths of 20 μm. In the near future, we would like to fabricate several different conducting polymer nanowires on a common substrate for sensing operations. Multiple sensors on a common substrate would result in a more functional sensor capable of sensing multiple analytes at very low concentrations

AI in Oncology - Precision Therapy & Prognosis

Artificial intelligence (AI) has strong logical reasoning abilities and the ability to learn on its own, and it can mimic the human brain's thought process. Machine learning and other AI technologies have the potential to greatly enhance the existing method of anticancer medicine development. However, AI currently has several limits. This study investigates the evolution of artificial intelligence technologies in anti-cancer therapeutic research, such as deep learning and machine learning. At the same time, we are optimistic about AI's future

3D Printed Bioreactor with Optimized Stimulations for Ex-Vivo Bone Tissue Culture

Motivation: Long term tissue survivability ex-vivo can greatly facilitate research on the influence of external stimulus (loading, radiation, microgravity) on the tissue, including mechanisms of disease transmission and subsequent drug discoveries. Bioreactors (used to culture living tissue ex-vivo) can be a valuable tool to study cell activity during physiological processes by mimicking their in-vivo native 3D environment.Objective Statement: We have developed a compact, 3D printed bioreactor equipped with both continuous flow-perfusion and dynamic mechanical-loading stimulations, capable of maintaining ex-vivo viability of swine cancellous bone cores over a long period. Qualitative study of the cultured cores (in terms of material composition and mechanical properties) has also been carried out.Materials and Method: Trabecular cores of 10mm diameter and 10mm height extracted from the femoral head of freshly sacrificed swine were cultured in the bioreactor. External stimulations of flow perfusion (15mL/h) and mechanical loading (35N at 0.22Hz for 1hour daily) were imparted to the cultured samples. Periodic analysis of tissue viability, mechanical stiffness and compositional make-up were carried out via Confocal Fluorescent Microscopy, Nanoindentation and Raman Spectroscopy respectively. To find the optimized flow rate for stimulation, effect of media perfusion was compared between 15mL/h, 35 mL/h and 60 mL/h flow rates. Ongoing work involves analyzing the effect of different loading signals to extract the optimized mechanical loading parameters.Result: Tissue survivability could be achieved up to 35 days of culture. Matrix composition could be best retained via combination of continuous flow perfusion and periodic mechanical loading. Increasing the perfusion rate to 60mL/h yielded best results in prolonging tissue survivability and in maintaining bone quality.Conclusion: We have characterized compositional and cell viability changes under exvivo storage of swine cancellous bone, with primary focus on development of the tissue culture platform and optimization of stimulation parameters. The 3D printed platform, equipped with both flow-perfusion and mechanical loading is capable of successfully culturing cancellous tissue up to 35 days

Scalable Model-Based Gaussian Process Clustering

Gaussian process is an indispensable tool in clustering functional data, owing to it's flexibility and inherent uncertainty quantification. However, when the functional data is observed over a large grid (say, of length $p$), Gaussian process clustering quickly renders itself infeasible, incurring $O(p^2)$ space complexity and $O(p^3)$ time complexity per iteration; and thus prohibiting it's natural adaptation to large environmental applications. To ensure scalability of Gaussian process clustering in such applications, we propose to embed the popular Vecchia approximation for Gaussian processes at the heart of the clustering task, provide crucial theoretical insights towards algorithmic design, and finally develop a computationally efficient expectation maximization (EM) algorithm. Empirical evidence of the utility of our proposal is provided via simulations and analysis of polar temperature anomaly (\href{https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/global/time-series}{noaa.gov}) data-sets