What ICoN-PCL intends to offer.
ICoN-PCL is funded and being built. Nothing on this page is open for booking yet, so this is the service catalog as designed: the capability being assembled, and the order it is expected to arrive in.
Synthesis
Making the material in the first place, and making it reproducibly enough that a downstream process can rely on it.
2D material growth
PlannedGrowth of transition metal dichalcogenides for electronic device work, with process conditions set by the remote user.
MXene synthesis
PlannedSynthesis from MAX precursors through etching, dispersion of 2D flakes in solution, drying, and ultrasonic mixing with pre-alloyed or elemental powders for downstream manufacturing.
Wafer-level deposition of 2D materials
PlannedAddressing direct growth and wafer-scale deposition, the prerequisites for using MXenes in 2D device manufacturing.
Nanofabrication
Remote-access deposition with in-situ characterization at every step, at the Birck Nanotechnology Center.
Cluster ALD with in-situ ellipsometry
PlannedGate dielectric deposition on a 2D material at user-defined temperature, pressure, and thickness, with film thickness monitored in situ.
In-situ Raman and photoluminescence
PlannedA dedicated chamber for monitoring the impact of dielectric deposition on the 2D material without breaking vacuum, enabling step-by-step inspection between deposition and characterization.
Gate metal and MXene deposition
PlannedSuccessive chambers for gate metal deposition and for ICoN-PCL-synthesized MXenes, with in-situ thickness characterization at each step.
In-situ XPS
PlannedStack inspection to identify exact stoichiometry, where interfacial chemical reactions significantly affect total stack properties.
Cleanroom processing
Available via BirckSpin coating, lithography and general nanofabrication across 220+ tools in the 25,000 sq ft class 1/10/100 cleanroom, accessible today through Purdue's recharge system.
Manufacturing
Autonomous workflows for non-equilibrium alloys at the Manufacturing and Materials Research Laboratories.
Laser-based additive manufacturing
PlannedMetal-MXene mixtures loaded into laser-based additive equipment, enabling custom 2D dispersions to be integrated during manufacture rather than added afterwards.
Sample preparation
PlannedWire electrical discharge machining for sectioning, plus CNC machining, integrated into the same workflow.
Autonomous heat treatment
PlannedA heat treatment queue with autonomous furnace control and sample handling.
Coating deposition
PlannedCold spray coating deposition, which can be inserted at any point in the workflow.
Mobile robotics and digital-twin sync
In designA multi-modal data pipeline built on ROS2 for real-time digital-twin synchronization, with mobile robots that do AI-driven decision-making, pathfinding and SLAM rather than pure logistics.
Characterization and testing
Including mechanical testing at temperatures few laboratories can reach.
High-temperature mechanical testing
PlannedAn inert-atmosphere, laser-based heating system connected to a micromechanical Psylotech loadframe, for autonomous testing at strain rates from 10−8 to 101 s−1 and from room temperature to 2,200 °C. Fatigue, creep, and quasi-static tensile.
Corrosion and oxidation
PlannedAutonomous aqueous corrosion testing and high-temperature oxidation, at room and elevated temperatures.
Structural characterization
PlannedSurface topography, residual stress, X-ray diffraction, and surface integrity measurement, executed as steps within an autonomous workflow.
Digital assets
Autonomous laboratories, standardized data capture, predictive digital twins, and a multi-agent research assistant, tied together so a facility behaves like a coordinated discovery engine rather than a rack of instruments.
ICoN Research Assistant
A multi-agent system that plans experiments with you and knows every asset's status and valid parameter ranges. It speaks Model Context Protocol, so other nodes' agents can reach ICoN-PCL assets and vice versa.
Digital twins
Twins of every instrument and sample, informed by real-time data, giving probabilistic predictions with quantified uncertainty. They double as safety guardrails for autonomous experimentation.
A laboratory DSL
A domain-specific language for specifying experiments declaratively, capturing sample provenance and checking safety limits against instrument metadata before anything runs.
FAIR data services
Metadata captured at the source, an extensible metadata core, persistent identifiers, RO-Crate packaging, and published analysis workflows as nanoHUB Sim2Ls.
Training and education
Three delivery modes, designed so a first-time user and a power user are not handed the same material.
Self-guided modules
Asynchronous tutorials and training materials delivered through nanoHUB, tagged with learning objectives, prerequisites and the instruments they apply to.
Consultations
One-on-one and small-group sessions with ICoN-PCL staff, who stay involved throughout a user's research rather than only at onboarding.
Interoperability workshops
A workshop series run jointly with other programmable cloud lab nodes and self-driving labs, beginning with a registry of physical and digital assets across the network.
Digital-twin environments let learners rehearse experimental planning and make decisions in realistic scenarios before touching sensitive or complex laboratory assets. Learning pathways differ by user type: first-time users, experienced researchers, prototype users, high-throughput users, power users, educators, and industry participants.
Roadmap
We expect at least three pieces of remotely programmable equipment by the end of the first year, one of them fully self-driving, growing to more than twenty by the end of the project, with ten running as self-driving instruments. Digital twins track the same curve, from three in the first year to twenty by the fourth.
The record they produce grows faster: from ten registered samples in year one (2026) to ten thousand by year four (2029), alongside two hundred FAIR simulation workflows and a hundred training and education resources.
Want a service that is not listed?
ICoN-PCL is designed to be expandable: partners will be able to register additional physical assets, machine-learning tools, physics-based simulations, or training material, after which those services become available to users and to the ICoN Research Assistant. Tell us what you need through Support.