Silicon Micro-Fabrication Technologies for Micro-Filters

2010 ◽  
Vol 2010 (1) ◽  
pp. 000498-000504 ◽  
Author(s):  
Bivragh Majeed ◽  
Wim De Malsche ◽  
Lei Zhang ◽  
Paolo Fiorini ◽  
Deniz Sabuncuoglu Tezcan ◽  
...  
Keyword(s):  
High Performance ◽  
Detection System ◽  
Plate Height ◽  
Micro Fabrication ◽  
Deep Reactive Ion Etching ◽  
Biological Mixtures ◽  
Successful Separation ◽  
Ordered Array ◽  
Definition Of ◽  
Si Wafer

Silicon micro-fabrication techniques allow for the development of microfluidic systems with very accurate control of size and uniformity of structures. In this paper we report on the silicon fabrication process of micro-filters for versatile application in fluidics systems. Micro-filters are composed of an ordered array of pillars and supply channels. Depending on pillar pitch, they can be used for, e.g., electrophoresis, chromatography and purification of biological mixtures. In this paper we focus on high performance liquid chromatography. The process that we have developed for micropillar fabrication consists of defining first 1μm diameter pillars with an inter-pillar distance of 1μm or less in an oxide hard mask with a DUV stepper, stitching is used to form few cm long patterns across the 200mm wafers. Second, the supply channels are defined with 1× alignment lithography. After definition of supply channels, deep reactive ion etching of silicon is performed with an optimised recipe to etch submicron pillars and supply channels of 100μm wide at the same time. The simultaneous etch of both structures avoids complex lithography steps otherwise necessary to protect the pillars while etching the supply channels or vice versa as would be done conventionally. Wafers are then anodically bonded to 200mm Pyrex wafers in order to seal the channels. Pyrex wafer also allows the use of optical detection system. Feed through holes for accessing the supply channels are etched on the backside of Si wafer. Filter characterization has been performed: a plate height of 1μm was measured and successful separation of 3 coumarin dyes is achieved.

High performance Nanogold™-in situ hybridzation and its use in the detection of hybridized and PCR-amplified microscopical preparations

1996 ◽  
Vol 54 ◽  
pp. 896-897
Author(s):  
G. W. Hacker ◽  
I. Zehbe ◽  
J. Hainfeld ◽  
A.-H. Graf ◽  
C. Hauser-Kronberger ◽  
...  
Keyword(s):  
Polymerase Chain Reaction ◽  
Messenger Rna ◽  
High Performance ◽  
Detection System ◽  
Direct Methods ◽  
Genetic Alterations ◽  
Chain Reaction ◽  
Protein Encoding ◽  
Polymerase Chain

In situ hybridization (ISH) with biotin-labeled probes is increasingly used in histology, histopathology and molecular biology, to detect genetic nucleic acid sequences of interest, such as viruses, genetic alterations and peptide-/protein-encoding messenger RNA (mRNA). In situ polymerase chain reaction (PCR) (PCR in situ hybridization = PISH) and the new in situ self-sustained sequence replication-based amplification (3SR) method even allow the detection of single copies of DNA or RNA in cytological and histological material. However, there is a number of considerable problems with the in situ PCR methods available today: False positives due to mis-priming of DNA breakdown products contained in several types of cells causing non-specific incorporation of label in direct methods, and re-diffusion artefacts of amplicons into previously negative cells have been observed. To avoid these problems, super-sensitive ISH procedures can be used, and it is well known that the sensitivity and outcome of these methods partially depend on the detection system used.

scholarly journals An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 656
Author(s):  
Xavier Larriva-Novo ◽  
Víctor A. Villagrá ◽  
Mario Vega-Barbas ◽  
Diego Rivera ◽  
Mario Sanz Rodrigo
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
High Performance ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Detection Techniques ◽  
Traffic Characteristics ◽  
Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

scholarly journals A validated quantification of triclosan in toothpaste using high-performance thin-layer chromatography and a 48-bit fatbed scanner

2021 ◽  
Author(s):  
Barbara Anders ◽  
Sabrina Doll ◽  
Bernd Spangenberg
Keyword(s):  
Acetic Acid ◽  
Thin Layer ◽  
Thin Layer Chromatography ◽  
High Performance ◽  
Detection System ◽  
Flatbed Scanner ◽  
Butyl Ether ◽  
Quantification Method ◽  
Direct Measurements ◽  
Layer Chromatography

AbstractWe present a densitometric quantification method for triclosan in toothpaste, separated by high-performance thin-layer chromatography (HPTLC) and using a 48-bit flatbed scanner as the detection system. The sample was band-wise applied to HPTLC plates (10 × 20 cm), with fluorescent dye, Merck, Germany (1.05554). The plates were developed in a vertical developing chamber with 20 min of chamber saturation over 70 mm, using n-heptane–methyl tert-butyl ether–acetic acid (92:8:0.1, V/V) as solvent. The RF value of triclosan is hRF = 22.4, and quantification is based on direct measurements using an inexpensive 48-bit flatbed scanner for color measurements (in red, green, and blue) after plate staining with 2,6-dichloroquinone-4-chloroimide (Gibbs' reagent). Evaluation of the red channel makes the measurements of triclosan very specific. For linearization, an extended Kubelka–Munk expression was used for data transformation. The range of linearity covers more than two orders of magnitude and is between 91 and 1000 ng. The separation method is inexpensive, fast and reliable.

scholarly journals Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study

2020 ◽  
Author(s):  
Yuchen Luo ◽  
Yi Zhang ◽  
Ming Liu ◽  
Yihong Lai ◽  
Panpan Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
High Performance ◽  
Detection System ◽  
Random Order ◽  
Colon Polyps ◽  
Clinical Environment ◽  
Polyp Detection ◽  
Link Type ◽  
Polyp Detection Rate

Abstract Background and aims Improving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Real-time automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment. Methods The selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists (> 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov. (NCT047126265). Results In this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p < 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p < 0.001), but no difference was found with regard to larger lesions. Conclusions A real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion. Trial Registration clinicaltrials.gov Identifier: NCT047126265

Fabrication of Three-Dimensional Micro-Structures with Two-Photon Absorption by Femtosecond Laser

2006 ◽  
Vol 532-533 ◽  
pp. 568-571
Author(s):  
Ming Zhou ◽  
Hai Feng Yang ◽  
Li Peng Liu ◽  
Lan Cai
Keyword(s):  
Photonic Crystal ◽  
Femtosecond Laser ◽  
Detection System ◽  
Three Dimensional ◽  
Photon Absorption ◽  
Micro Fabrication ◽  
Two Photon Absorption ◽  
Two Photon ◽  
Photo Polymerization ◽  
System A

The photo-polymerization induced by Two-Photon Absorption (TPA) is tightly confined in the focus because the efficiency of TPA is proportional to the square of intensity. Three-dimensional (3D) micro-fabrication can be achieved by controlling the movement of the focus. Based on this theory, a system for 3D-micro-fabrication with femtosecond laser is proposed. The system consists of a laser system, a microscope system, a real-time detection system and a 3D-movement system, etc. The precision of micro-machining reaches a level down to 700nm linewidth. The line width was inversely proportional to the fabrication speed, but proportional to laser power and NA. The experiment results were simulated, beam waist of 0.413μm and TPA cross section of 2×10-54cm4s was obtained. While we tried to optimize parameters, we also did some research about its applications. With TPA photo-polymerization by means of our experimental system, 3D photonic crystal of wood-pile structure twelve layers and photonic crystal fiber are manufactured. These results proved that the micro-fabrication system of TPA can not only obtain the resolution down to sub-micron level, but also realize real 3D micro-fabrication.

scholarly journals Development of a Rapid UHPLC-PDA Method for the Simultaneous Quantification of Flavonol Contents in Onions (Allium cepa L.)

2021 ◽  
Vol 14 (4) ◽  
pp. 310
Author(s):  
Ana V. González-de-Peredo ◽  
Mercedes Vázquez-Espinosa ◽  
Ceferino Carrera ◽  
Estrella Espada-Bellido ◽  
Marta Ferreiro-González ◽  
...  
Keyword(s):  
High Performance ◽  
Desirability Function ◽  
Radical Scavenging ◽  
Complete Separation ◽  
Raw Material ◽  
Antioxidant Compounds ◽  
Protective Effects ◽  
Quercetin Derivatives ◽  
Allium Cepa L ◽  
Successful Separation

Onion, one of the most consumed vegetables in the world, is also known to contain high levels of antioxidant compounds, with protective effects against different degenerative pathologies. Specifically, onion is rich in flavonols, mainly quercetin derivatives, which are compounds with high antioxidant and free radical scavenging power. For this reason, it is of the utmost importance to count on optimal analytical methods that allow for the determination and quantification of these compounds of interest. A rapid ultra-high performance liquid chromatography (UHPLC)-photo-diode array (PDA) method for the separation of the major flavonols in onions was developed using a Box–Behnken design in conjunction with multiresponse optimization on the basis of the desirability function. The conditions that provided a successful separation were 9.9% and 53.2% of phase B at the beginning and at the end of the gradient, respectively; 55 °C column working temperature; and 0.6 mL min−1 flow rate. The complete separation was achieved in less than 2.7 min with excellent chromatographic characteristics. The method was validated, and its high precision, low detection and quantification limits, good linearity, and robustness were confirmed. The correct applicability of the method improves the analysis of the raw material, increasing the quality of onions and its subproducts in terms of bioactive compounds and functional characteristics for consumers.

Sign in / Sign up

Export Citation Format

Share Document