The use of Polyvinylidene Fluoride (PVDF) based piezoelectric nanofibers for sensing and actuation has been reported widely in the past. However, in most cases, PVDF piezoelectric nanofiber mats have been used for sensing applications. This work fundamentally characterizes a single electrospun PVDF nanofiber and demonstrates its application as a sensing element for nanoelectromechanical sensors (NEMS). PVDF nanofiber mats were spun by far field electrospinning (FFES) process and complete material characterization was conducted by means of scanning electron microscope (SEM) imaging, Raman Spectroscopy and FTIR spectroscopy. An optimized recipe was developed for spinning a single suspended nanofiber on a specially designed MEMS substrate which allows the nano-mechanical and electrical characterization of a single PVDF nanofiber. Electrical characterization is conducted using a single suspended nanofiber to determine the piezoelectric coefficient (d33) of the nanofiber to be -58.77 pm/V. Also the mechanical characterization conducted using a nanoindenter revealed a Young’s Modulus and hardness of 2.2 GPa and 0.1 GPa respectively. Finally, an application that utilizes the single PVDF nanofiber as a sensing element to form a NEMS flow sensor is demonstrated. The single nanofiber flow sensor is tested in presence of various oscillatory flow conditions.

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This work demonstrates the application of electrospun single and bundled carbon nanofibers (CNFs) as piezoresistive sensing elements in flexible and ultralightweight sensors. Material, electrical, and nanomechanical characterizations were conducted on the CNFs to understand the effect of the critical synthesis parameter-the pyrolyzation temperature on the morphological, structural, and electrical properties. The mechanism of conductive path change under the influence of external stress was hypothesized to explain the piezoresistive behavior observed in the CNF bundles. Quasi-static tensile strain characterization of the CNF bundlebased flexible strain sensor showed a linear response with an average gauge factor of 11.14 (for tensile strains up to 50%). Furthermore, conductive graphitic domain discontinuity model was invoked to explain the piezoresistivity originating in a single isolated electrospun CNF. Finally, a single piezoresistive CNF was utilized as a sensing element in an NEMS flow sensor to demonstrate air flow sensing in the range of 5-35 m/s.

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Due to the high effectiveness of cancer screening and therapies, the diagnosis of second primary cancers (SPCs) has increased in women with breast cancer. The present study was conducted to develop a novel machine learning–based classification scheme for predicting the risk factors of SPCs in breast cancer survivors. The proposed scheme was based on the XGBoost classifier with the following four comparable strategies: transformation, resampling, clustering, and ensemble learning, to improve the training balanced accuracy. Results suggested that the best prediction accuracy for an empirical case is the XGBoost associated with the strategies of resampling and clustering. The experimental results showed that age, sequence of radiotherapy and surgery, surgical margins of the primary site, human epidermal growth factor, high-dose clinical target volume, and estrogen receptors are relatively more important risk factors associated with SPCs in patients with breast cancer. These risk factors should be monitored for the early detection of breast cancer. In conclusion, the proposed scheme can support the important influence of personality and clinical symptom representations in all phases of the primary treatment trajectory. Our results further suggested that adaptive machine learning techniques require the incorporation of significant variables for optimal predictions.

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