A Barristor Company | Graphene Barristor

Recently, the development of GFET based biosensors has led to successful real-time detection of the SARS-CoV-2 virus, responsible for COVID-19. These biosensors allow for the direct injection of patient samples (such as saliva or sputum) onto the sensor without preprocessing, confirming infection status through electrical signals. Moreover, graphene biosensors have shown the potential to detect trace amounts of beta-amyloid proteins present in the blood, enabling the diagnosis of age-related diseases such as Alzheimer's dementia. These sensors can analyze the quantity of specific proteins in a single drop of blood, confirming the presence of diseases, and they are highly sensitive, capable of detecting biomarkers at the picogram level.

Despite of the intense research on the application of graphene biosensors, graphene has limitations in fitting into conventional transistor structures and lacks sensitivity in terms of sensor performance. Graphene barristor can be a platform designed to apply graphene to electronic sensor devices. Unlike conventional graphene sensors, graphene barristor demonstrates performances suitable for industrial applications, and its structure suggesting its potential as an innovative biosensor with higher sensitivity than existing transistor-based sensors or PCR.

Previously developed transistor sensors could detect concentrations down to 0.1% of the Limit of Detection (LoD) of PCR, but their response was only around 0.1%. Even at the LoD concentration of PCR, they showed response of only 2% or less, making them unsuitable for point-of-care testing (PoCT). However, compared to graphene transistors, the "graphene barristor" has sensitivity over 1000 times higher and can detect concentrations down to 0.5% of PCR's LoD within 15 minutes. Especially at this concentration, there is a response difference of 540% between positive and negative patients (700% at PCR's LoD). Therefore, the graphene barristor is a technology applicable for rapid and highly sensitive on-site diagnosis.

Recently, the development of GFET based biosensors has led to successful real-time detection of the SARS-CoV-2 virus, responsible for COVID-19. These biosensors allow for the direct injection of patient samples (such as saliva or sputum) onto the sensor without preprocessing, confirming infection status through electrical signals. Moreover, graphene biosensors have shown the potential to detect trace amounts of beta-amyloid proteins present in the blood, enabling the diagnosis of age-related diseases such as Alzheimer's dementia. These sensors can analyze the quantity of specific proteins in a single drop of blood, confirming the presence of diseases, and they are highly sensitive, capable of detecting biomarkers at the picogram level.

Despite of the intense research on the application of graphene biosensors, graphene has limitations in fitting into conventional transistor structures and lacks sensitivity in terms of sensor performance. Graphene barristor can be a platform designed to apply graphene to electronic sensor devices. Unlike conventional graphene sensors, graphene barristor demonstrates performances suitable for industrial applications, and its structure suggesting its potential as an innovative biosensor with higher sensitivity than existing transistor-based sensors or PCR.

Previously developed transistor sensors could detect concentrations down to 0.1% of the Limit of Detection (LoD) of PCR, but their response was only around 0.1%. Even at the LoD concentration of PCR, they showed response of only 2% or less, making them unsuitable for point-of-care testing (PoCT). However, compared to graphene transistors, the "graphene barristor" has sensitivity over 1000 times higher and can detect concentrations down to 0.5% of PCR's LoD within 15 minutes. Especially at this concentration, there is a response difference of 540% between positive and negative patients (700% at PCR's LoD). Therefore, the graphene barristor is a technology applicable for rapid and highly sensitive on-site diagnosis.