EPFL Science Seed Fund

EPFL 2017 – Alain Herzog

EPFL Science Seed Fund

One of the EPFL missions is to foster outstanding scientific research. World-changing discoveries have often involved the collaboration of scientists working in different research areas. Through its open organization and culture, EPFL encourages scientists to meet, discuss, and come up with new ideas at the crossroads of their research fields and disciplines.

Limited access to campus since mid-March 2020, however, has slowed down interactions and scientific exchanges between EPFL scientists. In order to restore scientific exchange and encourage new collaborations, the EPFL Presidency has decided to dedicate funds in 2022 to a Science Seed Fund.

Eight projects were selected in 2022.

Bres, Camille (Photonic Systems Laboratory, STI)

Butté, Raphael (Laboratory of Advanced Semiconductors for Photonics and Electronics, SB)

The successful integration of diverse optical functionalities on a chip is essential to get compact and lower power devices, impacting a broad range of applications from telecommunication, to imaging and quantum systems. Integrated photonics based on silicon has revolutionized the field, benefiting from standard CMOS fabrication technology. However, silicon suffers from several significant drawbacks, which sparked research on alternative platforms.

This project aims at developing aluminum nitride (AlN)-on-sapphire waveguiding structures to respond to the need for integrating nonlinear functionalities. AlN stands out among semiconductors with one of the widest bandgaps, enabling applications from UV to mid-infrared. High quality crystalline AlN which can be epitaxially grown on sapphire substrates combine the thought-after 2nd and 3rd order nonlinearities, dispersion engineering capabilities and CMOS compatibility, setting AlN as a serious contender for chip-scale nonlinear optics.

By combining the expertise of PHOSL in the design, optimization, and characterization of nonlinear integrated photonic devices, with state-of-the-art know-how of LASPE on epitaxial growth, optical characterization and nano-fabrication of III-nitrides, this project will establish high-quality fabrication of AlN waveguides and microresonators, engineered for efficient nonlinear operation in the visible, telecom and mid-IR spectral band and strengthen EPFL position in integrated photonics.

Kippenberg, Tobias (Laboratory of Photonics and Quantum Measurements, SB/STI)

Carbone, Fabrizio (Laboratory for Ultrafast Microscopy and Electron Scattering, SB)

The manipulation of the interaction between light and free electrons is the seed for several applications ranging from advanced light sources, such as X-FELs, to novel methods for spectroscopy and sensing. Recent advances in both ultrafast transmission electron microscopy and integrated photonics hold promise for manipulating and exploiting the intimate quantum nature of light-electron interaction at the nano-scale.

This project aims at combining the expertise from two world leading groups in the fields, the laboratory for ultrafast microscopy and electron scattering (LUMES) and the laboratory of photonics and quantum measurement (LPQM), to develop a new platform for advanced experiments that combine integrated photonic circuits with ultrafast electron pulses.

Lacour, Stephanie (Foundation Bertarelli Chair in Neuroprosthetic Technology, STI)

Amstad, Esther (Soft Materials Laboratory, STI)

Axons are elongated fibers that transfer information throughout the 3D network of the nervous system.  These flexible and conducting tracts have no synthetic equivalent that combines mechanical compliance, electrical conductivity and 3D arrangement. 

The project is addressing this limitation by forming synthetic “axonal” networks.  This will be achieved by fabricating a soft hydrogel within which electrically conductive pathways can be patterned in situ. It will functionalize a hydrogel with Ag+ or Au+ ions and selectively reduce ions to form conducting paths composed of percolating silver or gold nanoparticles though localized UV light illumination.  This project will study the influence of the hydrogel composition and nanoparticle concentration, size, and arrangement on the mechanical properties and electrical conductivity of the resulting composite.  Tract patterning resolution will be assessed in the plane and in depth of the hydrogel volume. 

This knowhow will be employed to build demonstrator artificial axonal tracts that may be interfaced with neurospheres in vitro. 

Licina, Dusan (Human-Oriented Built Environment Lab, ENAC)

Nenes, Athanasios (Laboratory of atmospheric processes and their impacts, ENAC)

During the global COVID-19 pandemic, ultraviolet (UV) and ionization air disinfection devices have been widely implemented in buildings as an active cleaning technique to remove or inactivate airborne viruses. However, they can also induce a variety of unintended gas-phase chemical reactions of indoor air pollutants which may lead to acute and chronic effects on the human respiratory system and health. HOBEL and LAPI research groups propose to conduct a set of novel experiments to (1) to characterize the byproducts formation and transformation of gaseous air pollutants due to the operation of air disinfection devices under different indoor activities and assess occupants’ exposure; (2) to understand the influence of the operational conditions of building ventilation systems (e.g., humidity, ventilation rate) on the transformation processes; and (3) to investigate the fate and removal mechanisms of the air disinfection byproducts.

In a unique environmental chamber at EPFL Fribourg campus with advanced building control technologies, the gas- and particle-phase air pollutants will be monitored by the state-of-the-art online mass spectrometer and aerosol instruments. Two types of common and commercially available air disinfection products will be tested, including portable UV air cleaner and portable ionization air cleaner. This study is expected to provide new insights into the formation and fate of air disinfection byproducts and human exposure science.

Rahi, Sahand (Laboratory of the Physics of Biological Systems, SB)

Barth, Patrick (Laboratory of Protein and Cell Engineering, SV)

Checkpoints are molecular feedback systems that ensure the integrity of the cell cycle by preventing progression through the cell cycle when cells encounter errors or damage. Checkpoint integrity is critical for health, and dysfunction can cause numerous diseases, most prominently cancer. We propose to build optogenetic tools to control key proteins in the DNA damage checkpoint to be able to probe how checkpoints maintain and fail to arrest cells in the presence of persistent DNA damage.

Savona, Vincenzo (Laboratory of Theoretical Physics of NanosystemsSB)

Scarlino, Pasquale (Hybrid Quantum Circuits LaboratorySB)

Quantum computers leverage the laws of quantum physics to execute many computational tasks much more efficiently than conventional computers. Present and future quantum computers are affected by errors occurring at a rate high enough to compromise large computations. An emergent, promising method to correct these errors, consists in translating the quantum information into a language expressed in terms of the states of light – the so called “bosonic codes”. These codes make it possible to efficiently detect and correct only some among the most frequently occurring errors.

In the project supported by the EPFL Seed Fund, Pasquale Scarlino and Vincenzo Savona will develop a novel bosonic code: the Squeezed Cat Code. This code will enable for the first time the simultaneous detection and correction of all the most frequent errors. Their preliminary studies indicate that this bosonic code may outperform all existing ones and establish a new state of the art in quantum error correction. The two scientists will collaborate to define the details of the squeezed cat code, optimize its performance, and study its feasibility. The laboratory led by Pasquale Scarlino – a leading scientist in the study of hardware for quantum computers – will then explore the realization of a proof of principle of the code, on a mainstream quantum device, thus laying the foundations of a new paradigm for quantum computing.

Schmid, Alexandre (Biomedical and neuromorphic microelectronic systems, STI)

Matteo, Dal Peraro (Laboratory for Biomolecular Modeling, SV)

Nanopore sensing is a powerful single-molecule approach that enables the characterization of molecules of interest in a label-free, fast, and cheap way, having the potential of integration as portable devices. It has been successfully applied in many fields, including DNA sequencing, decoding digital information stored in polymers and protein sensing. However, the development of this technology has been hampered by the ability of commercial equipments to record very low ionic currents, in the order of pico- or femto-ampere.

This project aims at developing an integrated biological nanopore sensing platform with enhanced, unique features concerning miniaturization, noise level, bandwidth and throughput. This will have an impact for the development of portable diagnostic devices, next-generation information storage, and DNA and protein sequencing. To achieve this goal, the project will (1) design an integrated amplifier with high bandwidth and low noise features; (2) perform the complete characterization of the integrated system; and (3) develop multiple channels to achieve high-throughput readout. 

Tuia, Devis (Environmental Computational Science and Earth Observation Lab, ENAC)

Bosselut, Antoine (Natural Language Processing Lab, IC)

This project aims at enabling the access to the information contained in Earth observation (EO) images by interfacing EO science with natural language processing (NLP). This project will develop approaches to describe satellite images and their changes using natural language, yielding explanations that are accessible to all users. Once the descriptions are generated, they can be used to explain processes at work in the images or to answer user-related questions. To do so, the researchers will develop machine learning approaches rooted in an open set world, where the knowledge used to generate the explanations and answers are inferred from large textual corpora such as Wikipedia or specialised webpages.


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