Diabetes T1/T2
Objective
Cassiopée and its partners introduce a printable metal-organic-framework/graphene (MOF-G) sensor array that, when laminated onto a thin PET patch and clipped into a smartwatch-sized module, continuously measures glucose, sodium ions and pH in sweat while performing real-time self-calibration. The non-invasive patch device could be mounted on the arm and streams data to a smartphone via Bluetooth, giving users real time laboratory-grade information on hydration and metabolic status.
Impacts on Patients’ Quality of Life
Millions of people with diabetes still rely on invasive finger-prick tests or micro-needle implants, e.g., continuous glucose monitor, that track only glucose.
These enzyme-based systems suffer from infection risk, short lifetime and single-parameter output, leaving users blind to electrolyte loss and pH shifts that
precede heat-stress or cramp episodes.
Available glucose monitoring devices typically suffer from the following limitations:
(1) Need of direct blood sampling via needle skin penetration (invasive)
(2) Regular glucose sensor replacement(s)
(3) Skin sensors are not re-usable
(4) Expensive, e.g., CHF 130/month, not fully covered by insurance (in CH)
(5) Physical pain and (occasional) bleeding
(6) Non-invasive techniques/sensors do not provide continuous data readings.
Solution: MOF-G Technology
We fabricate nano-structured MOF-graphene (MOF-G) composite electrodes that combine the high catalytic activity of MOFs with the excellent conductivity and mechanical flexibility of graphene. HKUST1 (CuBTC) will serve as the model MOF-G because it can be synthesized rapidly at room temperature in water and easily scaled for industry.
The Key Problems Addressed
The first challenge is the low catalytic efficiency of existing enzyme free glucose sensors when they are deployed in human sweat. Although recent work has moved toward fourth generation, non-enzymatic devices that rely on direct electrocatalytic oxidation, and thus avoid costly enzymes, most prototypes are evaluated in alkaline media or in serum at pH 7.5–10. Because human sweat is mildly acidic (pH 3–8), such conditions do not reflect real world operation, leading to poor performance in practice.
The second, closely related problem is the strong pH dependence observed in metal-organic framework (MOF) based enzyme free sensors. MOFs offer large surface area, tunable porosity, and abundant unsaturated metal sites, all of which endow them with outstanding electrocatalytic potential.
For more infos and use of this diagnostic system, please, contact Cassiopée.
Practical Breakthrough in Non-Enzymatic Detection Systems
1. Achieves stable non-enzymatic glucose detection in weakly acidic sweat environments
Unlike the limitations of existing non-enzymatic sensors, which are mostly validated in buffer or simulated systems with pH above 5.5, this technology enables long-term reliable detection in real human sweat (pH 3–8) through the MOF-G composite structure and self-calibration system.
2. Collaborative Innovation in Multi-Parameter Integration and Self-Calibration
The innovation integrates glucose catalytic detection, Na⁺ ion sensing, and pH sensing into a single flexible patch. Rather than simply stacking multiple sensors, self-calibration is achieved through real-time linkage of the three-channel data. This integrated "detection-calibration" design addresses two core pain points long faced by wearable sweat sensors: the "one-sidedness of single-parameter detection" and "accuracy degradation due to environmental interference," providing a new paradigm for collaborative analysis of multiple physiological indicators.
3. System-Level Integration of Materials, Algorithms, and Hardware
Breaks through the single innovation dimension of "material performance optimization" and achieves systematic innovation in "MOF-G material catalytic performance enhancement + self-calibration error correction + flexible hardware integration." For example, by analyzing CV curves to optimize electrode reaction kinetics and combining human exercise trial data to establish a correlation model between electrolyte balance and metabolic state, the sensor data is upgraded from "numerical detection" to "health status interpretation," distinguishing it from traditional wearable sensors that only provide single-parameter indicators.