Research

My research advances industrial ecology by making life cycle assessment more useful before technologies, production systems, and infrastructure choices become locked in. I develop engineering-informed, prospective, and spatially differentiated models that connect technical evidence with decisions about design, scale-up, manufacturing, location, energy supply, facility integration, and operation.

Environmental performance is not a fixed property of a technology. It depends on process design, scale, facility operation, location, energy systems, and time.

Across pharmaceuticals, functional materials, cleanrooms, sanitation, and medical technologies, my published work connects engineering performance with environmental and economic consequences. Sustainable healthcare is a central application, supported by a broader research program in sustainable materials and technology deployment.

Methodological Contributions

Engineering-informed process assessment

I connect process models and inventories with LCA, life cycle costing, and scale-up scenarios. The molnupiravir study developed an integrated assessment of nine synthesis routes, including procedures for missing inventory data and tests of solvent recovery, electricity supply, and production scale. The carbon nanotube study combined technology screening, process modeling, and ex-ante LCA, including purification and alternative carbon sources. These contributions make engineering choices explicit in sustainability comparisons.

Evidence: Molnupiravir synthesis, PNAS and carbon nanotube production, Chemical Engineering Journal.

Spatial and prospective facility models

My cleanroom research links energy demand to cleanliness class, technical efficiency, local climate, and future electricity systems. The modeling framework compares locations and operating scenarios and provides a scenario calculator. Its contribution is to represent facilities as systems whose environmental performance changes with engineering requirements, geography, and time.

Evidence: Cleanroom sustainability, Cell Reports Sustainability.

Deployment screening linked to life cycle scenarios

The Sol-Char study combines random-forest suitability screening across 76 candidate countries with ex-ante LCA of baseline, international-transport, and local-construction scenarios. This connects the question of where a technology is suitable with the consequences of how it is manufactured and deployed. The approach brings regional conditions and implementation choices into early technology assessment.

Evidence: Sol-Char deployment, Resources, Conservation & Recycling.

Prospective impact characterization

In collaborative work on water scarcity, I contributed to research that forecasts future AWARE characterization factors with machine learning. The study benchmarks time-series models and compares forecasts with scenario-based factors. It extends prospective assessment beyond changing production inventories to the environmental conditions that determine impacts, and identifies the importance of distinguishing seasonal patterns from long-term change.

Evidence: Water-scarcity forecasting, Journal of Industrial Ecology.

Research Approach

Build credible engineering evidence. Define the service, reconcile mass and energy flows, and collect primary data for parameters that can alter a decision.
Anticipate development and deployment. Parameterize scale, location, technology maturity, facility operation, supply chains, and future energy systems.
Test decision robustness. Use scenarios, uncertainty, sensitivity, and economic assessment to identify stable choices, thresholds, and evidence gaps.

Computational modelling is the core platform. Targeted measurements, small-scale experiments, device disassembly, or material and process characterization can close important foreground-data gaps. This evidence loop allows environmental assessment to accompany technology development instead of entering only after a design has matured.

Application Domains

Sustainable healthcare systems

At Erasmus Medical Center (Erasmus University Rotterdam), I lead sustainability modeling for ECO-PATH. The work develops connected representations of hospital facilities, energy-intensive equipment, products, workflows, and clinical services to support decisions about environmental burden, clinical value, cost, equity, and implementation.

Sustainable pharmaceutical manufacturing

My pharmaceutical research spans active-ingredient synthesis, formulation, extraction, and biomanufacturing. It also draws on two years of industry experience.

Antiviral synthesis: comparative LCA and costing of nine molnupiravir routes, with attention to solvents, energy, recovery, and scale. Published study.
Antibiotics and inhalable anti-infectives: tobramycin, an antibiotic, and molnupiravir, an antiviral, in dual-drug dry powders. The study compares spray drying, spray freeze-drying, and supercritical antisolvent processing through particle characterization, machine learning, gate-to-gate LCA, and costing. Published study.
Bioactive compounds and extraction: assessment of extraction technologies, solvent choice, recovery, and process scale-up for natural bioactive compounds. Published review.
mRNA vaccines, ongoing research: collaboration with researchers at the University of Sheffield on energy consumption, environmental impacts, and techno-economic modeling of continuous mRNA vaccine production.

These studies are supported by my work on LCA methods and data needs in the pharmaceutical industry.

Sustainable materials and green chemistry

My materials research connects material function and manufacturing choices with environmental performance. It includes carbon utilization, functional chemicals, nanomaterials, and experimental water-treatment technologies.

Carbon nanomaterials: ex-ante assessment of carbon nanotubes produced from captured carbon, including conversion, purification, electricity, and scale-up choices. Published study.
Functional surfactants: collaborative studies with researchers at Montpellier and ECOIND combine synthesis LCA with evidence on material properties, biodegradation, and ecotoxicity for thiophene-based compounds. Synthesis and LCA; optoelectronic applications.
Silver nanoparticles: ultrasound-intensified continuous production evaluated through experimental process data, LCA, and machine learning, with antibacterial and photothermal applications. Published study.

My experimental foundation includes porous polymer microparticles for toluene-contaminated water and carbon nanotube/titanium dioxide composites for dye removal. This experience supports my work with material characterization and primary process data.

Medical devices and controlled facilities

I assess medical devices, clinical consumables, reusable systems, sterilization and disinfection workflows, cleanrooms, and other controlled environments. These cases connect product design with equipment use, facility services, procurement, logistics, safety, and end-of-life management.

Representative publications: Assessing the Environmental Impacts of Microfluidic Devices for Glucose Detection and A Sustainable-by-Design Wearable Sweat Sensor via Compostable Microfluidics and Laser-Induced Graphene Electrodes for Real-Time Monitoring of Physical Exercise, both in ACS Sustainable Chemistry & Engineering.

Comparative deployment cases

Sanitation, CCUS, renewable-energy communities, circular manufacturing, and carbon utilization provide comparative cases for testing spatial and temporal deployment methods beyond healthcare. They help distinguish reusable industrial ecology methods from conclusions that are specific to one sector.

Selected Programs and Leadership

PROGENY: Sustainability Analysis Lead for prospective environmental assessment of emerging bionic-device technologies and their fabrication systems.
LIFE-GREENAPI: Sustainability Analysis Lead across laboratory, pilot, and industrial scales for greener pharmaceutical process development.
STORCITO: CCUS Technology Lead for technical and sustainability assessment of rural carbon-management pathways.
RENvolveIT: Project Coordination Lead for an interdisciplinary toolbox supporting renewable-energy communities.
ECO-PATH: Sustainability Analysis Lead for reusable environmental models of hospital facilities, technologies, workflows, and care services.

Methods and Tools

My core methods include prospective, dynamic, and spatially differentiated LCA; life cycle costing and techno-economic assessment; material flow analysis; Scope 1 to 3 accounting; scenario, contribution, uncertainty, sensitivity, and Monte Carlo analysis; and engineering foreground modelling. I build reproducible workflows in Python using Activity Browser and Brightway, supported by process and energy-system models where appropriate.

Machine learning serves defined and validated tasks such as feasibility screening, parameter estimation, process optimization, and surrogate modelling. It supports engineering and life cycle analysis; it does not replace them.

Research Positioning

My research sits at the intersection of environmental engineering and industrial ecology. I develop and apply prospective life cycle methods across pharmaceutical manufacturing, healthcare systems, functional materials, and environmental technologies. The common question is how engineering evidence can guide design and deployment before major production and infrastructure decisions become fixed.

Selected Research Collaborations

My collaborations connect research groups working on process engineering, materials, life cycle methods, and healthcare systems.

Justin Z. Lian