Biomedical · Chemical · Catalytic

Predictive models for transport, reaction and binding

Valiant Scientific builds simulation and scientific machine learning tools for the systems where flow meets chemistry: microfluidic assays, reactors and catalytic surfaces. We turn first-principles physics and your experimental data into models you can design with.

Microfluidic slide with a 500 micrometre channel. A fluorescent green binding zone marks where analyte adsorbs to the surface, with a concentration colour bar from 0 to C0 and a handheld reader plotting capture efficiency against Péclet number.
Analyte transport and surface capture in a 500 µm microfluidic channel. Coupling advection–diffusion of concentration C with binding kinetics at the wall resolves the bound surface density Γ and how capture efficiency depends on the Péclet number.
Applications

Where we work

Our models share one core: coupled fluid flow, species transport and surface or bulk reactions. That common foundation lets us move quickly across three closely related fields.

Biomedical

Biosensors & lab-on-chip

Quantitative models of assay devices and biological transport, from channel design to calibrating binding constants against measured signal.

  • Microfluidic immunoassays and surface capture
  • Organ-on-chip and cell culture flow environments
  • Drug delivery and transport in tissue
  • Hemodynamics and wall shear stress
Chemical

Reactors & process design

Simulation-driven design and scale-up of chemical processes, with fast surrogate models for optimisation and control.

  • Reactor design, mixing and residence time distribution
  • Multiphase flow with heat and mass transfer
  • Continuous-flow and microreactor chemistry
  • Separation and purification processes
Catalytic

Surface reactions & catalysis

Linking surface kinetics to reactor-scale performance, so catalyst and support choices can be evaluated before they are built.

  • Heterogeneous catalysis and microkinetic modelling
  • Adsorption, desorption and site-coverage dynamics
  • Porous media, packed beds and washcoats
  • Electrocatalysis and electrode transport
Expertise

The methods behind the models

Our engineers and scientists bring years of experience in numerical analysis, computational fluid dynamics, computational statistics and machine learning, backed by solid software engineering and system integration.

Reacting-Flow CFD & Digital Twins

Coupled flow, transport and reaction solvers packaged as digital twins for real-time monitoring and optimisation of devices and processes.

Physics-Informed Deep Learning

Neural networks constrained by conservation laws and kinetic models, for fast surrogates and for recovering rate constants and parameters from sparse experimental data.

High Performance Computing

Scalable parallel solvers for large parameter sweeps, uncertainty quantification and design optimisation that would be impractical on a workstation.

Scientific Data Engineering

Pipelines that connect geometry, simulation output and lab measurements, from STL files to HDF5 and Zarr datasets ready for analysis and training.

Scientific ML Reimagined

Data-driven multiphysics

We pair rigorous physics with machine learning. That gives predictions you can trust outside the training data, at a speed that makes design exploration practical.

01

Model

Build first-principles models of the governing physics: advection–diffusion, reaction kinetics, adsorption isotherms and heat transfer.

02

Learn

Calibrate against your measurements with physics-informed learning, identifying unknown parameters and quantifying their uncertainty.

03

Scale

Deploy fast surrogates and HPC workflows to explore designs, optimise operating conditions and support decisions in real time.

Have a transport or kinetics problem?

Tell us about your device, reactor or catalyst and we'll scope how modelling can help.

Get in touch