Macro view of a Moseley molecular gas-sensing chip — copper sensing array, FBAR resonator and detected ethylene rising as a cyan glow
+ Ethylene Intelligence · Integrated Components

One ethylene intelligence platform.

Moseley integrates five components into a single ethylene intelligence platform: Professor Wu’s biomimetic ethylene receptor (Henan), Monash MOF remediation, E-Sentinel AI, a teacher-student learning architecture, and a digital twin framework — one closed loop that detects, predicts and remediates ethylene.

Biomimetic receptor (Henan) Monash MOF remediation E-Sentinel AI Teacher-student learning Digital twin framework
01 · One integrated platform

Five components, one ethylene intelligence platform.

Moseley is not a collection of separate technologies — it is a single ethylene intelligence platform built from five integrated components: Professor Wu’s biomimetic ethylene receptor developed at Henan Agricultural University, Monash University MOF remediation materials, E-Sentinel AI, a teacher-student learning architecture, and a digital twin framework. Together they form one closed loop. Source-of-truth teaches once. Inexpensive nodes infer everywhere.

01 Detect
Biomimetic ethylene receptor (Henan)
Developed at Henan Agricultural University, Professor Wu’s biomimetic ethylene receptor reads ethylene the way a plant does. A metal-cystine coordination complex array (Cu, Fe, Zn, Co, Ni, Mn) gives cross-channel redundancy. The chemistry is selective by design: the receptor mimics the plant’s own ethylene-binding site.
metal-cystinebiomimeticmulti-channel
02 Predict
E-Sentinel on-device ML
E-Sentinel is the Moseley on-device machine-learning model. It learns climacteric state and trajectory by training against a validated reference ethylene monitor during field trials, then runs entirely on the E-Aegis without that reference instrument. Its output is a calibrated risk score — not a ppb claim.
E-Sentinelteacher–studentrisk score
03 Remediate
Monash MOF remediation
When risk crosses a threshold, the remediation layer engages. Monash University metal-organic framework (MOF) materials hold ethylene captive; sentinel-gated thermal cycling regenerates capacity in place. Detection and remediation live inside the same host platform — the E-Aegis.
Monash MOFethylene removalsentinel-gated
02 · The chemistry

Receptor-inspired sensing — chosen because the plant chose it first.

In plants, the ethylene-binding site is a copper-containing receptor. Moseley uses metal-cystine coordination complexes — a small cysteine-based ligand bound to a transition metal — to recreate that selectivity in an engineered material. Cuprous variants give the strongest ethylene response; iron, zinc, cobalt, nickel and manganese variants extend the chemistry across humidity-tolerant operating envelopes.

The array is heterogeneous on purpose. Each metal centre responds to a different slice of the headspace. The ML model reads the pattern across the array, not any single channel — which is how it rejects drift, humidity transients and look-alike volatiles.

Remediation is handled by Monash University MOF materials in the E-Remediation layer, while a digital twin framework mirrors each deployment in software — so detection, prediction and remediation are coordinated as one integrated platform.

ppb
Detection floor
Parts-per-billion sensitivity, in a footprint that fits inside a 1U chassis or a hockey-puck node.
6
Metal centres
Cu, Fe, Zn, Co, Ni, Mn cystine variants — selected per target molecule and per moisture envelope.
SDK
Open chemistry
The platform is receptor-agnostic. New sensing elements drop into the array; the model retrains.
03 · The architecture

One host platform. Four operational layers.

The E-Aegis is the Moseley host platform — a 1U-class chassis that contains the sensing, intelligence and remediation layers in a single integrated unit. E-Conditioning, E-Array, E-Sentinel and E-Remediation are not separate products. They are layers inside E-Aegis.

L1 E-Conditioning
Sample conditioning
Inlet filtration, humidity buffering, flow control and temperature stabilisation. The conditioning layer presents a clean, repeatable sample stream to the sensing array — the foundation that makes everything above it possible.
filtrationhumidityflow
L2 E-Array
Metal-cystine sensor array
The metal-cystine coordination complex sensor array sits directly downstream of the conditioning layer. Multiple metal centres, multiple sensing elements per metal, one reading per element per second. This is the molecular eye of the platform.
biomimeticmulti-metal1 Hz
L3 E-Sentinel
On-device ML model
E-Sentinel reads the array’s response, applies the teacher-trained model, and emits a calibrated risk score expressing climacteric state and trajectory. Runs entirely on the host MCU. No cloud round-trip required. How E-Sentinel is trained →
TinyMLedgerisk score
L4 E-Remediation
Active removal layer
A multi-layer adsorbent system inside the same chassis. When E-Sentinel calls for intervention, sentinel-gated thermal cycling exposes fresh sorbent surface. The target molecule is captured and held. Capacity is reported back to the cloud; the cycle repeats.
multi-layersentinel-gatedthermal
04 · The learning model

Source-of-truth teaches once. Inexpensive nodes infer everywhere.

A laboratory-grade reference ethylene monitor — relatively expensive, large, commercially deployed — rides along with the E-Aegis on training voyages. Paired data flows back to the Moseley training pipeline: reference readings on one side, E-Array responses on the other.

E-Sentinel learns the relationship. The reference is then removed. E-Aegis goes to work on its own.

A distilled TinyML student of E-Sentinel then runs on every E-MCP release node and every E-Nose Label pallet sensor — nodes that carry no ethylene sensor of their own, and do not need one. The student infers climacteric state from temperature, humidity, time-in-deployment, BLE neighbour signals and adsorbent loading state.

This is the architectural claim: a Moseley node does not have to measure ethylene to act on it. Read the full teacher–student story →

1
Reference instrument
Pairs with E-Aegis during training trials. Removed after the model converges.
N
Distributed nodes
E-MCP and E-Nose Label run distilled models. No reference. No ethylene sensor. Just inference.
42d
Operational floor
Every Moseley node is designed to run unattended for at least the length of a reefer journey.
05 · The differentiator

E-Remediation — active intervention, not just a louder alarm.

Detection without action is just a louder alarm. The E-Remediation layer is the part competitors don't have — a multi-layer adsorbent system, sentinel-gated by E-Sentinel, that holds target molecules captive and regenerates in place. All inside the same chassis as the sensor.

01 · Sense
E-Array reads the headspace
The metal-cystine sensor array reports a multi-channel response at one reading per element per second.
02 · Predict
E-Sentinel scores the risk
The on-device ML model separates real signal from drift and humidity transients, scores climacteric state and trajectory, and decides whether to remediate.
03 · Remediate
E-Remediation captures
Sentinel-gated cycling routes the headspace through fresh adsorbent. Target molecules bind and stay.
04 · Report
Capacity reported to cloud
Adsorbent loading state is reported back via the E-Platform telemetry layer. The cycle is ready to repeat for the rest of the journey.
Inside a refrigerated container — palletised fresh produce monitored by distributed Moseley sensor nodes, the operating environment for E-Aegis, E-MCP and E-Nose Label
+ Reach   One host platform · many distributed nodes · every cold corridor
06 · Form factors

Same architecture. Different chassis.

The four-layer stack — conditioning, sensing, prediction, remediation — is the constant. The chassis changes to match where the ethylene risk lives and what role the node plays in the fleet.

E-Aegis — the 1U host platform. Carries the full stack. Trained against a reference instrument; deployed inside reefer containers, cool rooms and distribution centres.

E-MCP — distributed release node. Hockey-puck form factor. Runs a distilled student of E-Sentinel. Carries no ethylene sensor — infers climacteric state from temperature, humidity, time, BLE neighbours and adsorbent loading state.

E-Nose Label — sticker-class pallet sensor. Carries the leanest student model. One per pallet, single-use, compostable.

One firmware family. One cloud platform. One model architecture. The chemistry and the chassis swap.

TWIN
Digital twin
A digital twin framework mirrors each deployment, fusing live ethylene signal with ripening trajectory across the fleet.
CHAIN
Cold chain use
Reefer, trailer, cool room, distribution centre. Ethylene, ripening volatiles, climacteric trajectory.
API
Cloud delivery
REST + MQTT to the Moseley E-Platform, or stream into your existing SCADA / WMS.
07 · IP & patents

A patent portfolio behind every layer of the stack.

Provisional patent applications filed under Ambient IoT Pty Ltd — sole inventor Tony Raftis — covering the host platform, every operational layer, the sensing chemistry and the teacher–student learning architecture. Built in deliberate parallel to the receptor-inspired chemistry described by Xu et al., Nature Communications 2026, and extended into multi-month cold-chain operating environments.

CLIM
Moseley Climacteric ArchitectureUmbrella filing · AU 2026904577
AEGIS
E-AegisHost platform · AU 2026904657
ARRAY
Metal-Cystine Sensor ArrayGenus filing, 47 claims · AU 2026904494
SENT
E-Sentinel MLTeacher–student inference architecture · AU 2026903645
REM
E-RemediationMulti-layer adsorbent, sentinel-gated · AP33-E-REMEDIATION-AU01
MCP
E-MCP V2Sentinel-gated multi-bed device · AU 2026904490
NOSE
E-Nose Pallet LabelSticker-class distributed sensor · AU 2026903651
PLAT
E-PlatformFleet telemetry & control · AU 2026903866

Want to go deeper into the architecture?

Technical briefs, sensor datasheets, and integration specs available under NDA. Get in touch and we'll set up a conversation with the engineering team.

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