About

Jean-François Gaillard is a Professor of Civil and Environmental Engineering at Northwestern University, with a courtesy appointment in Earth and Planetary Sciences. His primary research focuses on understanding the molecular and biogeochemical processes that influence the fate of metals in aquatic systems. He emphasizes elucidating how the chemical speciation of metals controls their biouptake and bioavailability, particularly in microbial species. His research involves conducting field studies, laboratory experiments, and modeling approaches utilizing advanced analytical, microscopic, and spectroscopic methods. Gaillard's recent activities have concentrated on the chemical fate of arsenic, nickel, and mercury in aquatic environments, as well as the environmental impacts of nanostructured materials used in various applications. His work aims to contribute to environmental chemistry and biogeochemical cycling of metals and metalloids, with a focus on metal speciation and environmental remediation. He has authored numerous publications in this field and is actively engaged in advancing knowledge on the environmental chemistry of metals and their interactions in aquatic systems.

Research topics

• Biology
• Chemistry
• Environmental chemistry
• Cell biology
• Ecology
• Nanotechnology
• Materials science
• Biochemical engineering
• Biophysics
• Organic chemistry
• Biochemistry
• Molecular biology

Selected publications

• Drinking water treatment residuals reduce toxicity to fish from metal-contaminated sediments

  Environmental Toxicology and Chemistry · 2025-01-06 · 1 citations

  article

  Drinking water treatment residuals (DWTRs) produced as a result of the coagulation-flocculation process during water treatment are considered waste materials. Characterization of this material shows its ability to sequester metals and other anionic and cationic chemicals. Drinking water treatment residuals from two different drinking water treatment plants located in Wyoming and Oregon were evaluated for their ability to function as viable capping materials of metal-contaminated sediments. The contaminated sediments tested were either spiked with a mixture of metals, 1 mg/kg of Cu, Zn, Cd, and Pb, or coming from an intertidal sediment collected at a U.S. Naval Air Station. A Gust chamber experiment was used to determine metal fluxes from these sediments into the overlying water with applied hydrodynamic stress of 0.05 and 0.4 Pa in the absence and presence of DWTR as a capping material. The DWTR effectively reduced the amount of metal released to the overlying waters to a value below the National Recommended Aquatic Life Criteria for Cr, Cu, Pb, and Zn, but slightly above the value for Cd. The toxicity of these waters was tested with an in vivo 96 hr fathead minnow survival assay. In the absence of capping, all the fry died within 1 hr. Capping with DWTR from Wyoming effectively reduced contamination, and 95% of the fish survived. The DWTR from Oregon was less successful, but the survival of fish was equivalent to diluting the original contaminated waters by a factor of 100. Drinking water treatment residual effectively reduced metallothionein in fish, a biomarker of metal contamination, corroborating the survival experiments. These results suggest that DWTRs may be very effective for remediation of metal-contaminated sites.

  PublisherDOI

• Active learning-guided optimization of cell-free biosensors for lead testing in drinking water

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-08-20 · 1 citations

  preprintOpen access

  Point-of-use diagnostics based on allosteric transcription factors (aTFs) are promising tools for environmental monitoring and human health. However, biosensors relying on natural aTFs rarely exhibit the sensitivity and selectivity needed for real-world applications, and traditional directed evolution struggles to optimize multiple biosensor properties at once. To overcome these challenges, we develop a multi-objective, machine learning (ML)-guided cell-free gene expression workflow for engineering aTF-based biosensors. Our approach rapidly generates high-quality sequence-to-function data, which we transform into an augmented paired dataset to train an ML model using directional labels that capture how aTF mutations alter performance. We apply our workflow to engineer the aTF PbrR as a point-of-use diagnostic for lead contamination in water. We tune the sensitivity of PbrR to sense at the U.S. Environmental Protection Agency (EPA) action level for lead and modify the selectivity away from zinc, a common metal found in water supplies. Finally, we show that the engineered PbrR functions in freeze-dried cell-free reactions, enabling a diagnostic capable of detecting lead in drinking water down to ~5.7 ppb. Our ML-driven, multi-objective framework-powered by directional tokens-can generalize to other biosensors and proteins, accelerating the development of synthetic biology tools for biotechnology applications.

  PublisherOA PDFDOI

• Active learning-guided optimization of cell-free biosensors for lead testing in drinking water

  Nature Communications · 2025-12-20 · 5 citations

  articleOpen access

  Point-of-use diagnostics based on allosteric transcription factors (aTFs) are promising tools for environmental monitoring and human health. However, biosensors relying on natural aTFs rarely exhibit the sensitivity and selectivity needed for real-world applications, and traditional directed evolution struggles to optimize multiple biosensor properties at once. To overcome these challenges, we develop a multi-objective, machine learning (ML)-guided cell-free gene expression workflow for engineering aTF-based biosensors. Our approach rapidly generates high-quality sequence-to-function data, which we transform into an augmented paired dataset to train an ML model using directional labels that capture how aTF mutations alter performance. We apply our workflow to engineer the aTF PbrR as a point-of-use diagnostic for lead contamination in water. We tune the sensitivity of PbrR to sense at the U.S. Environmental Protection Agency (EPA) action level for lead and modify the selectivity away from zinc, a common metal found in water supplies. Finally, we show that the engineered PbrR functions in freeze-dried cell-free reactions, enabling a diagnostic capable of detecting lead in drinking water down to ~5.7 ppb. Our ML-driven, multi-objective framework powered by directional tokens can generalize to other biosensors and proteins, accelerating the development of synthetic biology tools for biotechnology applications.

  PublisherDOI

• Ultrasensitive Water Contaminant Detection with Transcription Factor Interfaced Microcantilevers

  ACS Nano · 2025-02-24 · 2 citations

  article

  Water contamination by harmful chemicals is a growing global concern, creating a need to develop technologies that can detect a range of target compounds at the required concentrations. Here, we address this need by merging biological allosteric transcription factors with DNA-coated nanomechanical microcantilevers to detect chemicals in water with a digital readout. After proof-of-concept demonstration and optimization to detect anhydrotetracycline with the TetR transcription factor, we use the CadC transcription factor to detect Pb2+ and Cd2+ in water at concentrations down to 2 and 1 ppb, respectively, in less than 15 min. A computational model suggests this improvement in sensitivity could be achieved by the DNA-coated microcantilever surface changing the transcription factor binding properties. Our findings demonstrate a promising approach for water quality monitoring with fast, highly sensitive, digital readout.

  PublisherDOI

• Dissolution kinetics of copper oxide nanoparticles in presence of glyphosate

  NanoImpact · 2024-01-01 · 1 citations

  articleSenior authorCorresponding
  PublisherDOI

• Réduction des oxydes de fer, déstockage du carbone des sols noyés, et émission de méthane par un réservoir tropical

  HAL (Le Centre pour la Communication Scientifique Directe) · 2024-01-01

  articleOpen access

  International audience

  PublisherOA PDF

• A Sensor for Detecting Aqueous Cu²⁺ That Functions in a Just-Add-Water Format

  ACS Omega · 2024-12-19 · 3 citations

  articleOpen access

  There is growing concern around the negative health impacts associated with contamination of drinking water by harmful chemicals. Technology that enables fast, cheap, and easy detection of ions and small molecules in drinking water is thus important for reducing the incidence of these negative health impacts. Here, we describe a sensor for detecting Cu2+ in water that provides colorimetric results in 15 min or less and functions in a just-add-water format. The sensor contains cheap reagents including salts, buffer, oxidant, chromogen, surfactant, and optionally a chelating agent. The sensor is assembled and lyophilized for shelf-stability and field-deployment. Rehydrating the sensor with water containing Cu2+ results in chromogen oxidation and blue color formation to visually indicate the presence of Cu2+. The sensor demonstrates high selectivity toward Cu2+ against other metal cations, functionality in field samples, shelf-stability, and can be tuned to activate at different Cu2+ threshold concentrations. This sensor thus has the potential to meet a variety of needs, such as point-of-need testing for Cu2+ to ensure water supplies meet health guidelines, such as the United States Environmental Protection Agency’s Lead and Copper Rule.

  PublisherDOI

• Redressement d’une base régionale interopérable issue de données des SPSTI

  Archives des maladies professionnelles et de médecine du travail/Archives des maladies professionnelles et de l'environnement · 2024-05-01

  article1st author
  PublisherDOI

• AND GHG EMISSION FROM SOILS FLOODED BY A TROPICAL RESERVOIR

  2024-01-30

  articleOpen access
  Publisher

• Evaluating drinking water treatment residuals as an in-situ capping material for metal-contaminated sediments

  Chemosphere · 2024-11-01

  article
  PublisherDOI

Recent grants

• Biouptake of Mercury: Speciation and Processes at the Cell Surface

  NSF · $300k · 2013–2017

Frequent coauthors

• Didier Perret

  Natural Resources Canada

  29 shared

• Martial Taillefert

  28 shared

• Christophe Rabouille

  Université Paris-Saclay

  25 shared

• David A. Stahl

  University of Washington

  22 shared

• Marco A. Alsina

  20 shared

• Charles-Philippe Lienemann

  IFP Énergies nouvelles

  19 shared

• Aaron I. Packman

  Northwestern University

  19 shared

• Kimberly A. Gray

  18 shared

Education

• Doctorat Es-Sciences

  Université Paris Diderot