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Drago Strle   Professor  University Educator/Researcher 
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Drago Strle published an article in December 2017.
Top co-authors See all
I. Muševič

118 shared publications

Condensed Matter Department, J. Stefan Institute, Jamova 39, SI-1000 Ljubljana, Slovenia

Bogdan Štefane

60 shared publications

Faculty of Chemistry and Chemical Technology, University of Ljubljana, Večna pot 113, SI-1000 Ljubljana, Slovenia

Johannes Sturm

17 shared publications

CUAS, Josef Ressel Center for Integrated CMOS RF Systems and Circuits Design, Villach, Austria

E. Zupanič

12 shared publications

Jozef Stefan Institute, Jamova 39, SI-1000 Ljubljana, Slovenia

Marijan Maček

12 shared publications

University of Ljubljana

9
Publications
13
Reads
0
Downloads
11
Citations
Publication Record
Distribution of Articles published per year 
(2011 - 2017)
Total number of journals
published in
 
7
 
Publications See all
Article 5 Reads 1 Citation Chemical Selectivity and Sensitivity of a 16-Channel Electronic Nose for Trace Vapour Detection Drago Strle, Bogdan Štefane, Mario Trifkovič, Marion Van Mid... Published: 08 December 2017
Sensors, doi: 10.3390/s17122845
DOI See at publisher website ABS Show/hide abstract
Good chemical selectivity of sensors for detecting vapour traces of targeted molecules is vital to reliable detection systems for explosives and other harmful materials. We present the design, construction and measurements of the electronic response of a 16 channel electronic nose based on 16 differential microcapacitors, which were surface-functionalized by different silanes. The e-nose detects less than 1 molecule of TNT out of 10+12 N2 molecules in a carrier gas in 1 s. Differently silanized sensors give different responses to different molecules. Electronic responses are presented for TNT, RDX, DNT, H2S, HCN, FeS, NH3, propane, methanol, acetone, ethanol, methane, toluene and water. We consider the number density of these molecules and find that silane surfaces show extreme affinity for attracting molecules of TNT, DNT and RDX. The probability to bind these molecules and form a surface-adsorbate is typically 10+7 times larger than the probability to bind water molecules, for example. We present a matrix of responses of differently functionalized microcapacitors and we propose that chemical selectivity of multichannel e-nose could be enhanced by using artificial intelligence deep learning methods.
Article 0 Reads 0 Citations Design, simulation, and implementation of an integrated, hybrid photocurrent-to-digital converter in CMOS technology Uroš Nahtigal, Drago Strle Published: 01 September 2016
Integration, doi: 10.1016/j.vlsi.2016.08.002
DOI See at publisher website
Article 0 Reads 0 Citations Integrated High Resolution Digital Color Light Sensor in 130 nm CMOS Technology Drago Strle, Uroš Nahtigal, Graciele Batistell, Vincent Chi ... Published: 22 July 2015
Sensors, doi: 10.3390/s150717786
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
This article presents a color light detection system integrated in 130 nm CMOS technology. The sensors and corresponding electronics detect light in a CIE XYZ color luminosity space using on-chip integrated sensors without any additional process steps, high-resolution analog-to-digital converter, and dedicated DSP algorithm. The sensor consists of a set of laterally arranged integrated photodiodes that are partly covered by metal, where color separation between the photodiodes is achieved by lateral carrier diffusion together with wavelength-dependent absorption. A high resolution, hybrid, ∑∆ ADC converts each photo diode’s current into a 22-bit digital result, canceling the dark current of the photo diodes. The digital results are further processed by the DSP, which calculates normalized XYZ or RGB color and intensity parameters using linear transformations of the three photo diode responses by multiplication of the data with a transformation matrix, where the coefficients are extracted by training in combination with a pseudo-inverse operation and the least-mean square approximation. The sensor system detects the color light parameters with 22-bit accuracy, consumes less than 60 μA on average at 10 readings per second, and occupies approx. 0.8 mm2 of silicon area (including three photodiodes and the analog part of the ADC). The DSP is currently implemented on FPGA.
Article 0 Reads 0 Citations Vapor trace detection of different molecules in air Drago Strle, Bogdan Stefane, Igor Muševič Published: 05 May 2015
SPIE Newsroom, doi: 10.1117/2.1201504.005948
DOI See at publisher website
Article 4 Reads 7 Citations Sensitivity Comparison of Vapor Trace Detection of Explosives Based on Chemo-Mechanical Sensing with Optical Detection a... Drago Strle, Bogdan Štefane, Erik Zupanič, Mario Trifkovič, ... Published: 27 June 2014
Sensors, doi: 10.3390/s140711467
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
The article offers a comparison of the sensitivities for vapour trace detection of Trinitrotoluene (TNT) explosives of two different sensor systems: a chemo-mechanical sensor based on chemically modified Atomic Force Microscope (AFM) cantilevers based on Micro Electro Mechanical System (MEMS) technology with optical detection (CMO), and a miniature system based on capacitive detection of chemically functionalized planar capacitors with interdigitated electrodes with a comb-like structure with electronic detection (CE). In both cases (either CMO or CE), the sensor surfaces are chemically functionalized with a layer of APhS (trimethoxyphenylsilane) molecules, which give the strongest sensor response for TNT. The construction and calibration of a vapour generator is also presented. The measurements of the sensor response to TNT are performed under equal conditions for both systems, and the results show that CE system with ultrasensitive electronics is far superior to optical detection using MEMS. Using CMO system, we can detect 300 molecules of TNT in 10+12 molecules of N₂ carrier gas, whereas the CE system can detect three molecules of TNT in 10+12 molecules of carrier N₂.
Conference 0 Reads 0 Citations Sensor system for vapor trace detection of explosives Drago Strle, J. Trontelj, B. Stefane, I. Musevic Published: 01 October 2012
2012 IEEE Sensors, doi: 10.1109/icsens.2012.6411041
DOI See at publisher website
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