Light-based sensors could speed up head injury diagnosis

By Published On: 24 June 2026
Light-based sensors could speed up head injury diagnosis

A light-based sensor can detect head injury biomarkers at extremely low levels and could help doctors make faster diagnoses and treatment decisions.

The chip-based technology uses a metasurface, an ultra-thin material patterned with tiny features that manipulate light in ways conventional lenses cannot.

Coating a metasurface with antibodies that bind to specific target molecules allows it to detect those molecules using light.

Guangyuan Li, research team leader at the Beijing Institute of Technology, Zhuhai, said: “Although several biomarkers have been validated as indicators of TBI, current methods for measuring them are time-consuming and require multiple complex laboratory steps.

“To address this challenge, we developed metasurface biosensors that are exceptionally sensitive, allowing them to produce clear, reliable optical signals even when only tiny amounts of a biomarker are present.”

The team, jointly led by Li and Yunhui Liu of the Shenzhen Institute of Technology, CAS, developed the sensor to detect two traumatic brain injury biomarkers.

Biomarkers are measurable substances in the body that can indicate an injury or disease.

The device detected glial fibrillary acidic protein, known as GFAP, and S100 calcium-binding protein beta, or S100β, at levels as low as femtograms per millilitre.

A femtogram is roughly one quadrillionth of a gram.

The researchers said the platform could also be adapted to detect several biomarkers at the same time.

Liu said: “If developed into a point-of-care format, this technology could help provide faster and accurate answers after brain injury, perhaps using just a finger prick.

“This could potentially reduce unnecessary CT scans for low-risk cases while flagging higher-risk patients earlier.

“It could also enable more accessible biomarker detection in ambulances, rural clinics, sports settings or emergency departments where time matters.”

Detecting TBI biomarkers at clinically useful levels requires a sensor that is highly sensitive to low concentrations of molecules.

The researchers developed a device based on a corrugated gold surface with a high-quality, or high-Q, factor.

When light shines on the surface, its reflection spectrum shows an extremely narrow dip.

When target molecules bind to the treated surface, they slightly change its refractive index, which describes how light travels through a material.

This shifts the wavelength of the dip. Because the dip is very narrow, the sensor can distinguish even a small change and detect extremely low concentrations.

The team developed a precise nanofabrication technique to create the periodic gold pattern while reducing surface roughness and the loss of optical signals.

Li said: “We also developed a stable surface chemistry approach that allowed specific capture of the target molecules with low nonspecific adsorption and an optical setup that collects spectra with high signal-to-noise.

“Together, these innovations made it possible to create ultra-sensitive biosensors in a compact chip.”

To test the platform, the researchers prepared separate sensors coated with antibodies targeting S100β and GFAP.

They tested each biomarker at concentrations ranging from one femtogram per millilitre to 100 nanograms per millilitre.

The non-target TBI biomarkers H-FABP and UCH-L1 were used as controls.

The platform showed clear wavelength changes as the concentration of each target increased and achieved detection limits below one femtogram per millilitre for S100β and GFAP.

Its responses to the target biomarkers were markedly greater than its responses to the controls.

The researchers said the process used to make the gold metasurfaces is scalable but remains expensive, although work is under way to reduce costs.

Packaging and fluid-handling systems must also be improved before the technology can be considered for clinical use.

The sensor will need to be tested using more complex, clinically relevant samples.

Further studies involving patient groups will also be needed to assess its repeatability, robustness and performance in real-world settings.

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