Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency.
Date: 28.02.2024
Speaker: Isabelle Dicaire (CCTT Optech)
Topic: From Ariadnas to Industry R&D in optics and photonics
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Isabelle Dicaire - Research Fellow in Earth System Science, ACT 2012-2015
Ariadna 1: Spaceborne laser filamentation for atmospheric remote sensing
Ariadna 2: Detection and analysis of climate tipping points using genetic algorithms
Ariadna 3: The Silky Way: Biomimetic sensing through changes in structural proteins
BACKGROUND
* In collaboration with ACT colleagues:
Francisco Fernandez-Navarro & Nasia
Nikolaou
* Outcome: 5 papers published
* In collaboration with ACT colleague:
Tom Gheysens
* Outcome: 6 papers published
* Outcome: 2 papers published
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BIOMIMETIC SENSING THROUGH CHANGES IN STRUCTURAL PROTEINS
Project aim for the Ariadna study: proof-of-concept study of biosensing using spider silk
1st step: Spider “milking” to extract the dragline silk – done by Tom at the Oxford Silk Group
2nd step: Injecting light from an optical fiber into the spider silk – done by the Group for Fiber Optics at EPFL
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DESCRIPTION OF ARIADNA STUDY THE SILKY WAY:
3rd step: Measure the optical properties of the silk – done by the Group for Fiber Optics at EPFL
4th step: Proof-of-concept of breath analysis through polarimetry using spider silk
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DATA ANALYSIS METHOD OF CHOICE – OPTECH.ORG
Physics-based
Chemometrics &
Machine Learning
Optical
data
Physics-
Based
Model Chemistry
data
6. Non-profit organization in operation since 2002 |
Located in Montréal, Canada
Specialized in optics & photonics 2 < TRL < 8
• Industrial product & process developement
• Applied Research
• Measurement services
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Optech: bridging the gap between academia and industry R&D
TRL:
1 2 3 4 5 6 7 8 9
Pull
Industry
R&D
Push
Academic
R&D
Terrile, Richard J., et al. "Calibrating the technology readiness level (TRL) scale using NASA
mission data." 2015 ieee aerospace conference. IEEE, 2015.
$37 $31
$373 $363
$1249 $1262
y = 14,13e0,8211x
2 3 4 5 6 7
Investments
(k$
CAD)
Technology Readiness Level (TRL)
DEVELOPMENT COSTS
OPTECH PROJECT K.
Series1 Expon. (Series1)
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Optech: bridging the gap between academia and industry R&D
31 employees: physicists, engineers, technicians + admin staff
140+ projects done with more than 60 private companies in 2022
Network of 2400 employees that supports 6000+ companies per year
+10M$ in equipment with 600 m2 of laboratories
Only 8% of our revenues = recurrent public funding
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FIBER OPTICS AND SENSORS
11
Development of a palladium
nanoparticles ink for hydrogen
sensing applications
3D printed palladium
layer on an optical fiber
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FIBER OPTICS AND SENSORS
12
Proof-of-concept of quantum
optics sensing using 3D-printed
upconversion nanoparticles
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MICROSYSTEMS
13
Technologies Piezoelectric HAPT FBG (Proba 2) DBR Fiber laser
Pressure resolution (mBar) 13 2 0.03
Pressure resolution (%FS 0-25 Bar) 0.05% 0.01% 0.00015%
Temperature (K) 0.5K/300K 0.05 0.004-0.1
Precision on the propellant measurement
(months)
7(42) for a 10(15)-year
mission
N/D 1(5) - 3(16) for a 10(15-
year mission
OSIP project: Improving satellite
propellant gauging accuracy with high
accuracy Optical Pressure Sensors and
Ultrafast Optoelectronics
ESA Contract No.
4000138372/22/NL/GLC/ov
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MICROSYSTEMS
14
OSIP project: Improving satellite propellant gauging
accuracy with high accuracy Optical Pressure Sensors and
Ultrafast Optoelectronics
ESA Contract No. 4000138372/22/NL/GLC/ov
Base package
(Aluminum, Kovar)
Lid
Membrane
(Sapphire/Silicon)
DBR fiber laser
Fitting for pressure controller
seal cover
5 assembled units
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ARTIFICIAL INTELLIGENCE AND OPTICS
Confidentiel
Provide precise quantitative information for embedded
metrology applications from prototype design to
manufacturing and tests:
❖ K-means clustering analysis
❖ PLS-DA classification
❖ Dimension reduction with PCA
❖ Machine learning – Training of classification and
regression models (decision tree, k nearest neighbors,
logistic regression, SVM, gradient boosting, AdaBoost,
Random Forest, CatBoost, MLP, XGB, etc)
❖ Deep learning for imaging applications
DevOps prototypes: deployment of models in production,
application monitoring and continuous improvements