Thai University RankingsRESEARCH RADAR
← Back to research database
งานใหม่ที่น่าจับตา

BifunctionalLaser-Induced Graphene Platforms forIntegrated Electrochemical Sensing and Matrix-Free LDI-MS Quantitationof Sarcosine

IMPACT SIGNAL73/100
01

Information from the abstract

Abstract This work introduces a dual-modality analytical platform based on laser-induced graphene (LIG) for the label-free, noninvasive detection of the prostate cancer biomarker sarcosine in human urine. The tunable porosity and high conductivity of LIG enable its bifunctional application as both a sensitive electrochemical electrode and an efficient matrix for laser desorption/ionization mass spectrometry (LDI-MS). Functionalized with sarcosine oxidase and gold nanoparticles (AuNPs), the platform leverages the strengths of both modalities: the AuNPs/LIG electrode provides rapid electrochemical screening with a low limit of detection (LOD) of 2 μM, while the LDI-MS modality offers highly selective, matrix-free confirmation with an enhanced ultralow LOD of 500 nM. Both modalities share a wide linear range (5–50 μM), falling well below the clinical urinary threshold of 20 μM. By combining speed and simplicity from electrochemical sensing with the high sensitivity and molecular specificity of MS analysis on a single device, this platform demonstrates the orthogonal capabilities of LIG for robust, pretreatment-free cancer biomarker diagnostics.

02

Why this record is monitored

This record has an Impact Signal of 73/100 based on recency, source, collaboration, and bibliographic signals. It prioritizes monitoring and is not a judgment of research quality.

Related topics: Electrochemical sensors and biosensors · Mass Spectrometry Techniques and Applications · Advanced Nanomaterials in Catalysis

03

Thai researcher and institutional participation

Wimala Karintrithip · Pumidech Puthongkham · Pranee Rattanawaleedirojn · Narong Praphairaksit · Nadnudda Rodthongkum · Chulalongkorn University

04

Data limitations

This page is a bibliographic record based on abstract-level information, not a full analysis or quality assessment. Verify the DOI and original article before citation.