@article{annayev_biliroglu_sennik_yamaner_oralkan_2024, title={Fabrication of 32 x 32 2D CMUT Arrays on a Borosilicate Glass Substrate With Silicon-Through-Wafer Interconnects Using Non-Aligned and Aligned Anodic Bonding}, volume={8}, ISSN={["1941-0158"]}, url={https://doi.org/10.1109/JMEMS.2024.3440191}, DOI={10.1109/JMEMS.2024.3440191}, journal={JOURNAL OF MICROELECTROMECHANICAL SYSTEMS}, author={Annayev, Muhammetgeldi and Biliroglu, Ali Onder and Sennik, Erdem and Yamaner, Feysel Yalcin and Oralkan, Omer}, year={2024}, month={Aug} }
 @article{zhou_dieffenderfer_sennik_aleem_speight_vasisht_oralkan_lee_misra_2023, title={Performance of A Monolithic E-Nose Array Integrating MEMS and ALD Processing}, ISSN={["1930-0395"]}, DOI={10.1109/SENSORS56945.2023.10325054}, abstractNote={We demonstrate a novel electronic nose (E-nose), which combines microelectromechanical systems (MEMS) and atomic layer deposition (ALD) technologies. MEMS micromachining creates a monolithic microheater array, consisting of independently controlled rows. By changing temperature profiles, a wide range of sensing surfaces are available. Sensor electrodes are arranged in crossbars with microheater rows. SnO2 thin film is deposited on this array as sensing materials by ALD. This E-nose demonstrates excellent fundamental operating characteristics such as speed and repeatability. It is ultra-sensitive against multiple volatile organic compounds (VOCs). It can also intrinsically separate VOC mixtures by tuning its operating modes.}, journal={2023 IEEE SENSORS}, author={Zhou, Yilu and Dieffenderfer, James and Sennik, Erdem and Aleem, Mahaboobbatcha and Speight, Jakob and Vasisht, Shrey and Oralkan, Omer and Lee, Bongmook and Misra, Veena}, year={2023} }
 @article{sennik_kinoshita-millard_oh_kafer_dean_oralkan_2023, title={Plant Disease Detection Using an Electronic Nose}, ISSN={["1930-0395"]}, DOI={10.1109/SENSORS56945.2023.10325015}, abstractNote={This paper presents experimental results on differentiating between healthy wheat plants and plants infected with Fusarium Head Blight (FHB) based on sensing the ambient gases in the plant environment using a gravimetric electronic nose enabled by a functionalized capacitive micromachined ultrasonic transducer (CMUT) array and machine learning (ML) algorithms. The CMUT sensor array is functionalized with organic/inorganic materials to capture disease-related volatile signals. The sensor data is processed and analyzed using ML algorithms for accurate plant classification. Experimental results demonstrate the effectiveness of the proposed approach in achieving high accuracy for plant disease detection at the end of the 11th day after plant inoculation.}, journal={2023 IEEE SENSORS}, author={Sennik, Erdem and Kinoshita-Millard, Samuel and Oh, Yeonyee and Kafer, Christopher W. and Dean, Ralph A. and Oralkan, Omer}, year={2023} }
 @article{belekov_bautista_annayev_adelegan_biliroglu_kierski_sanders_kemal_sennik_yamaner_et al._2022, title={Performance Assessment of Ultra-Wideband and Dual-Mode 1D CMUT Arrays for Acoustic Angiography}, ISSN={["1948-5719"]}, DOI={10.1109/IUS54386.2022.9958537}, abstractNote={In this work, we have demonstrated the imaging potential of 256-element ultra-wideband (UWB) and dual-mode CMUT 1D arrays for acoustic angiography through mechanical index measurements and in-vitro imaging experiments. We have designed a custom 256-channel imaging probe with integrated low-noise amplifiers and supporting power circuitry. To improve the elevational focusing, we mounted an acoustic lens on to the array. The acoustic characterization of the CMUT array was performed by a calibrated hydrophone, with which we measured sufficiently high mechanical indices (i.e., 0.79 MI for the UWB and 0.85 MI for the dual-mode array) at the focal spot at 15-mm depth. We conducted an imaging experiment with a tissue-mimicking phantom including a 0.2-mm-diameter cellulose tube, in which microbubbles and water flowed. We demonstrated a CTR of 62.12 ± 1.06 dB for the UWB array and a CTR of 59.69 ± 0.39 dB for the dual-mode array when microbubbles were flowing through the tube. These experiments presented a strong use case for the UWB and dual-mode CMUT arrays in acoustic angiography applications.}, journal={2022 IEEE INTERNATIONAL ULTRASONICS SYMPOSIUM (IEEE IUS)}, author={Belekov, Ermek and Bautista, Kathlyne J. and Annayev, Muhammetgeldi and Adelegan, Oluwafemi J. and Biliroglu, Ali O. and Kierski, Thomas M. and Sanders, Jean L. and Kemal, Remzi E. and Sennik, Erdem and Yamaner, Feysel Y. and et al.}, year={2022} }
 @article{sennik_erden_constantino_oh_dean_oralkan_2021, title={Electronic nose system based on a functionalized capacitive micromachined ultrasonic transducer (CMUT) array for selective detection of plant volatiles}, volume={341}, ISSN={["0925-4005"]}, url={https://doi.org/10.1016/j.snb.2021.130001}, DOI={10.1016/j.snb.2021.130001}, abstractNote={Here, a small, low-power, wireless gas sensor platform for selective detection of volatile organic compounds (VOCs) released from plants under different abiotic or biotic stress conditions is described. This sensor platform is implemented based on a capacitive micromachined ultrasonic transducer (CMUT) array, in which elements were functionalized with a variety of materials including polymers, phthalocyanines, and metals to improve selectivity. Input impedance measurements of the functionalized CMUT array were compared to pre-coating measurements to analyze the mechanical loading. The CMUT arrays were then exposed to VOCs known to be emitted by plants with different concentrations under dry air flow at room temperature. The results demonstrated that 1-Octanol created the strongest response across different channels and a resolution of 3-ppb was calculated for the CMUT element functionalized using silver ink when exposed to 1-Octanol. The relative responses of different channels to tested volatiles were observed to be different. The k-nearest neighbor (k-NN) algorithm was used for the gas classification by dividing the data to training and test groups. The k-NN results showed that the gases at low concentrations were successfully classified with better than 97 % accuracy. Finally, to emulate the ambient atmosphere for plants, the gas tests were repeated by adding different levels of humidity to the gas flow. With a minimum 98 % accuracy, the k-NN classifier demonstrated that the functionalized CMUT array can be used for selective detection of the group of plant VOCs used in this study, even at different relative humidity levels in the ambient atmosphere.}, journal={SENSORS AND ACTUATORS B-CHEMICAL}, publisher={Elsevier BV}, author={Sennik, Erdem and Erden, Fatih and Constantino, Nasie and Oh, YeonYee and Dean, Ralph A. and Oralkan, Omer}, year={2021}, month={Aug} }
 @article{constantino_oh_sennik_andersen_warden_oralkan_dean_2021, title={Soybean Cyst Nematodes Influence Aboveground Plant Volatile Signals Prior to Symptom Development}, volume={12}, ISSN={["1664-462X"]}, DOI={10.3389/fpls.2021.749014}, abstractNote={Soybean cyst nematode (SCN), Heterodera glycines, is one of the most destructive soybean pests worldwide. Unlike many diseases, SCN doesn't show above ground evidence of disease until several weeks after infestation. Knowledge of Volatile Organic Compounds (VOCs) related to pests and pathogens of foliar tissue is extensive, however, information related to above ground VOCs in response to root damage is lacking. In temporal studies, gas chromatography-mass spectrometry analysis of VOCs from the foliar tissues of SCN infested plants yielded 107 VOCs, referred to as Common Plant Volatiles (CPVs), 33 with confirmed identities. Plants showed no significant stunting until 10 days after infestation. Total CPVs increased over time and were significantly higher from SCN infested plants compared to mock infested plants post 7 days after infestation (DAI). Hierarchical clustering analysis of expression ratios (SCN: Mock) across all time points revealed 5 groups, with the largest group containing VOCs elevated in response to SCN infestation. Linear projection of Principal Component Analysis clearly separated SCN infested from mock infested plants at time points 5, 7, 10 and 14 DAI. Elevated Styrene (CPV11), D-Limonene (CPV32), Tetradecane (CPV65), 2,6-Di-T-butyl-4-methylene-2,5-cyclohexadiene-1-one (CPV74), Butylated Hydroxytoluene (CPV76) and suppressed Ethylhexyl benzoate (CPV87) levels, were associated with SCN infestation prior to stunting. Our findings demonstrate that SCN infestation elevates the release of certain VOCs from foliage and that some are evident prior to symptom development. VOCs associated with SCN infestations prior to symptom development may be valuable for innovative diagnostic approaches.}, journal={FRONTIERS IN PLANT SCIENCE}, author={Constantino, Nasie and Oh, Yeonyee and Sennik, Erdem and Andersen, Brian and Warden, Michael and Oralkan, Omer and Dean, Ralph A.}, year={2021}, month={Sep} }