2021 IEEE 21st International Conference on Nanotechnology (NANO)最新文献

{"title":"Highly selective room temperature operated ammonia sensor synthesized using electrospun yttrium doped SnO2 nanofibers","authors":"Utkarsh Nirbhay, Ajay Beniwal, S. Lalwani, Sunny","doi":"10.1109/NANO51122.2021.9514344","DOIUrl":"https://doi.org/10.1109/NANO51122.2021.9514344","url":null,"abstract":"Yttrium (Y) doped SnO2 nanofibers were successfully synthesized and used for detecting low ammonia concentrations at room temperature (RT). Electrospinning followed by calcination method was used to synthesize the Y doped SnO2 nanofibers for various Y concentrations, among which $5 wt$.% Y doped SnO2 nanofibers (average diameter ~90 nm) demonstrated the best response. To analyze the selectivity of the sensor, the sensing properties were also studied for other analytes like acetone, methanol and ethanol, along with ammonia. The% response was observed to be 237%under 10 ppm of ammonia, which is found to be 2.7, 5.3 and 6.6 times higher as compared to acetone (87.5%), ethanol (44.4%) and methanol (36%) responses at 10 ppm, respectively, defining the excellent selectivity of the sensor towards ammonia detection. The fabricated sensor manifests fast response and recovery times i.e. less than a minute. The structural and morphological characteristics of Y doped SnO2 nanofibers were characterized using X-ray diffraction (XRD) and scanning electron microscopy (SEM), respectively.","PeriodicalId":6791,"journal":{"name":"2021 IEEE 21st International Conference on Nanotechnology (NANO)","volume":"12 1","pages":"151-154"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84976320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Polymer Carbon Dots for Next Generation White Light Emitting Devices","authors":"Manasa Perikala, A. Bhardwaj","doi":"10.1109/NANO51122.2021.9514290","DOIUrl":"https://doi.org/10.1109/NANO51122.2021.9514290","url":null,"abstract":"Fluorescent Carbon dots (CDs), a young member of Carbon nanomaterial family has gained a lot of research attention across the globe due to its highly luminescent emission properties, non-toxic behavior, stable emission properties and zero re-absorption losses. These dots have potential to replace the use of traditional semiconductor quantum dots in white light emitting devices. For successful application of these dots in White LEDs it is highly essential to synthesize luminescent CDs and CDphosphor with higher Quantum yields (QY). Immense research is going on across the globe in enhancing the optical properties of CDs and CD phosphor by proper surface functionalization and passivation of CD surface. In this paper we report successful fabrication of single system white light emitting CDs, CD phosphor and two different schemes to enhance the Quantum yield of fabricated CDs and CD phosphor by modifying the CD surface. The so fabricated CDs and CD phosphor emit white light under UV -illumination. Further the fabricated CDs and CD phosphor are characterized using UV-absorption emission spectroscopies and the quality of white light obtained from CDs and CD phosphor is characterized using CIE chromatic co-ordinates.","PeriodicalId":6791,"journal":{"name":"2021 IEEE 21st International Conference on Nanotechnology (NANO)","volume":"28 1","pages":"356-359"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76176292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impedance Based Biosensor for Agricultural Pathogen Detection","authors":"Rhea Patel, M. Vinchurkar, Rajul S. Patkar, Gopal Pranjale, M. Baghini","doi":"10.1109/NANO51122.2021.9514277","DOIUrl":"https://doi.org/10.1109/NANO51122.2021.9514277","url":null,"abstract":"One of the major limitations on food resources worldwide is the deterioration of plant products due to pathogenic infections. Early screening of plants for pathogenic infections can serve as a boon in the Agricultural sector. The standard microbiology techniques have not kept pace with the rapid enumeration and automated methods for bacteria detection. Electrochemical impedance spectroscopy (EIS) serves as a label free bio sensing technique to monitor pathogens in real time. The changes in the electrical impedance of a growing bacterial culture can be monitored to detect activity of microorganisms. In this study, we demonstrate development of a gold interdigitated electrode (gold IDE) based impedance biosensor to detect bacterial cell enrichment in a growth medium. To standardize the impedance measurement protocol, nutrient broth suspended E.coli cells were used as a model system. The changes in the magnitude of impedance is about 1.5MΩ per doubling of E.coli cells. We further extended this strategy to identify the pathogens in real samples using milk as cell growth medium. Distinct difference of about 5MΩ was seen in the impedance recorded for the healthy and infected potato samples. Our results support the potential application of this impedance based biosensor in agricultural pathogen detection","PeriodicalId":6791,"journal":{"name":"2021 IEEE 21st International Conference on Nanotechnology (NANO)","volume":"34 1","pages":"385-388"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80062211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Tunable Wide Flat-Top Band-Pass Plasmonic Filter based on Tilted T-Junction Resonators at Near-Infrared","authors":"Seyed Morteza Ebadi, Jonas Örtegren","doi":"10.1109/NANO51122.2021.9514278","DOIUrl":"https://doi.org/10.1109/NANO51122.2021.9514278","url":null,"abstract":"A highly efficient and compact wide flat-top band-pass filter at NIR is realized in a MIM plasmonic waveguide. Besides, simulation results reveal that through tuning the length of resonators, a broadband band-pass transmission can be easily achieved.","PeriodicalId":6791,"journal":{"name":"2021 IEEE 21st International Conference on Nanotechnology (NANO)","volume":"179 1","pages":"54-55"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77006355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrothermal Parameters of Graphene Nanoplatelets Films","authors":"A. Maffucci, F. Bertocchi, S. Chiodini, F. Cristiano, L. Ferrigno, G. Giovinco, S. Sibilia, G. Trezza","doi":"10.1109/NANO51122.2021.9514274","DOIUrl":"https://doi.org/10.1109/NANO51122.2021.9514274","url":null,"abstract":"This paper investigates the electrothermal behavior of industrial films made by graphene nanoplatelets (GNP) mixed to polymers, with the final goal of deriving their equivalent electrical resistivity and thermal emissivity. The two parameters are identified by means of suitable models and experimental characterization with ad-hoc test-fixture. The materials analyzed here are thin strips of the order of 1×10 cm, with a thickness of 75 µm, both made by pressed GNPs, mixed to a small quantity of polymeric binders. An analytical model to describe the temperature-dependence of the electrical resistivity is proposed. The impact of the binder percentage (5% or 20%) on the electrical resistivity and thermal emissivity is also studied.","PeriodicalId":6791,"journal":{"name":"2021 IEEE 21st International Conference on Nanotechnology (NANO)","volume":"5 1","pages":"323-326"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73711223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fuzzy-logic-inspired Multi-contrast-agent Strategy for Optimal Tumor Classification","authors":"Zheng Gong, Yifan Chen, Yue Sun, Yue Xiao, M. Cree","doi":"10.1109/NANO51122.2021.9514336","DOIUrl":"https://doi.org/10.1109/NANO51122.2021.9514336","url":null,"abstract":"This paper proposes a new fuzzy-logic-inspired multi-contrast-agent strategy (MCAS) for optimal tumor classification. The proposed strategy accounts for the competitive and symbiotic relationships among multiple contrast agents through a sequential logic circuit analysis. Furthermore, the strategy enables an intuitive yet systematic way to analyze the tumor classification vagueness and ambiguous uncertainties and optimize the utilization of multiple agents through a fuzzy comprehensive evaluation. A numerical example is used to demonstrate how the classification performance in terms of decision-making fuzziness is significantly improved with an optimal “cocktail recipe” methodology using the proposed MCAS.","PeriodicalId":6791,"journal":{"name":"2021 IEEE 21st International Conference on Nanotechnology (NANO)","volume":"142 1","pages":"314-317"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77838988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reservoir Computing System using Biomolecular Memristor","authors":"Md Razuan Hossain, J. Najem, Tauhidur Rahman, Md. Sakib Hasan","doi":"10.1109/NANO51122.2021.9514305","DOIUrl":"https://doi.org/10.1109/NANO51122.2021.9514305","url":null,"abstract":"Reservoir Computing (RC) is a highly efficient machine learning algorithm specially suited for processing temporal dataset. RC system extracts features from input by projecting them into a high dimensional space. A major advantage of RC framework is that it only requires the readout layer to be trained which significantly reduces the training cost for complex temporal data. In recent years, memristors have become extremely popular in neuromorphic applications due to their attractive analogy to biological synapses. Alamethicin-doped, synthetic biomembrane can emulate key synaptic functions due to its volatile memristive property which can enable learning and computation. In contrast to its solid-state counterparts, this two-terminal biomolecular memristor features similar structure, switching mechanism, and ionic transport modality as biological synapses while consuming considerably lower power. In this work, we have shown biomolecular memristor-based reservoir system to solve tasks such as classification and time-series analysis in a simulation based environment. Our work may pave the way towards highly energy efficient and biocompatible memristor-based reservoir computing systems capable of handling complex temporal tasks in hardware in the near future.","PeriodicalId":6791,"journal":{"name":"2021 IEEE 21st International Conference on Nanotechnology (NANO)","volume":"22 1","pages":"116-119"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73704905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-performance VOx-based memristors with ultralow switching voltages prepared at room temperature","authors":"M.Y. Wang, D. Wang, X.D. Huang","doi":"10.1109/NANO51122.2021.9514359","DOIUrl":"https://doi.org/10.1109/NANO51122.2021.9514359","url":null,"abstract":"In this work, the Ti/VOx/ITO memristors fabricated at room temperature with self-current compliance characteristics are demonstrated. The devices exhibited excellent resistive switching behavior with ultralow programming voltages (as low as 17 mV) and good retention time (>104 s). In addition, low-power characteristic with 36.72 nW set power (2.16 µA@17 mV) and 2.74 µW reset power (17.1 µA@0.16 V) were obtained in these memristive devices, which makes them promising in the next-generation information storage and computing applications.","PeriodicalId":6791,"journal":{"name":"2021 IEEE 21st International Conference on Nanotechnology (NANO)","volume":"158 1","pages":"468-469"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73957796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of HZO and HSO thin film ferroelectric on FDSOI NCFET","authors":"R. Shaik, K. P. Pradhan","doi":"10.1109/NANO51122.2021.9514307","DOIUrl":"https://doi.org/10.1109/NANO51122.2021.9514307","url":null,"abstract":"In this work, negative capacitance effect of MFMIS type FDSOI NCFET is investigated considering two well known thin film ferroelectric materials HZO (Zirconium doped HfO2) and HSO (Silicon doped HfO2). The investigations are carried out in a TCAD environment where the gate charge is extracted from the TCAD simulation and subsequently computed ferro voltage across the ferroelectric capacitor to find the effective gate voltage in the gate-stack. The obtained values are then subjected to variation in ferroelectric thickness to predict the onset of hysteresis for both HZO and HSO ferroelectric materials. It has been observed that the HZO ferroelectric offers superior improvement in sub-threshold slope (SS), peak-gm and off-current at lower ferroelectric thickness with the expense of gate-induced-drain-leakage (GIDL) and lower endurance to hysteresis. On the other hand, the HSO ferroelectric predicts improvement in SS, peak-gm and off-current with superior control in GIDL where the device has higher endurance towards hysteresis in-spite of ferroelectric thickness under sub-10 nm regime.","PeriodicalId":6791,"journal":{"name":"2021 IEEE 21st International Conference on Nanotechnology (NANO)","volume":"119 1","pages":"126-129"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74527497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Size Based Characterization of Gold Nano Particles using Machine Learning Approach","authors":"P. Senoamadi, S. Krishnannair, L. Sikhwivhilu","doi":"10.1109/NANO51122.2021.9514311","DOIUrl":"https://doi.org/10.1109/NANO51122.2021.9514311","url":null,"abstract":"In drug delivery, there is a need for precision in reporting particles parameters. Studies have shown that absorbency of drugs in the blood stream depends on the size of the nanoparticle. The shape and size of nanoparticles (NPs) matter the most, hence the distribution of NP depends on the size and shape of NPs. By synthesizing and characterizing the NPs, we are able to cluster and get the amount of a certain type of morphology and accurate size determination. Moreover, the size distribution of a particle plays a more important goal as it possesses an increase in the usability of a diagnostic and therapeutic tool in medicine. The shape and size distribution of NPs is important for the delivery of drugs and for the cure or treatment of several chronic diseases such as cancer. Hence it is important to get the accurate size distribution of NPs for better results. Gold nano particles (AuNPs) where measured manually by the use of transmission electron microscope, hence, in most cases human error could play part in terms of inaccurate measurements. The digital images of AnNPs contain noise, making it difficult to get accurate measurements using the transmission microscope. AuNPs were measured in terms of their width and length. This study focused on the characterization of AuNPs collected by the transmission electron microscope using machine learning approaches. Image preprocessing and processing techniques are used for extracting the features (length and width) of AuNPs. In this study, filtering techniques such as Gaussian blur, Median and Mean filtering techniques are employed for noise removal to increase the precision in estimating the size of NPs. Unsupervised machine learning algorithm such as K-means and Otsu are used to perform image segmentation of the filtered nano images for the accurate extraction of particles' features such as length and width. The size measurements obtained using the machine learning approaches are compared with the measurements taken by the transmission electron microscope (TEM) for error estimation in the size distributions of NPs. The results showed that machine learning approaches provided accurate measurements of most of the NPs as compared to TEM. Therefore, it is recommended that machine learning approaches can be used to estimate the size of NPs so that the shapes can be described better and classified during the synthesis process.","PeriodicalId":6791,"journal":{"name":"2021 IEEE 21st International Conference on Nanotechnology (NANO)","volume":"19 1","pages":"239-242"},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86243960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}