Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is used for the multiplex detection and characterization of diverse analytes over a wide mass range directly from tissues. However, analyte coverage with MALDI MSI is typically limited to the more abundant compounds, which have m/z values that are distinct from MALDI matrix-related ions. On-tissue analyte derivatization addresses these issues by selectively tagging functional groups specific to a class of analytes, while simultaneously changing their molecular masses and improving their desorption and ionization efficiency. We evaluated electrospray deposition of liquid-phase derivatization agents as a means of on-tissue analyte derivatization using 2-picolylamine; we were able to detect a range of endogenous fatty acids with MALDI MSI. When compared with airbrush application, electrospray led to a 3-fold improvement in detection limits and decreased analyte delocalization. Six fatty acids were detected and visualized from rat cerebrum tissue using a MALDI MSI instrument operating in positive mode. MALDI MSI of the hippocampal area allowed targeted fatty acid analysis of the dentate gyrus granule cell layer and the CA1 pyramidal layer with a 20-μm pixel width, without degrading the localization of other lipids during liquid-phase analyte derivatization.

A dopamine-modified TiO2 monolith was developed to assist LDI MS imaging for small metabolites and lipids simultaneously with enhanced sensitivity.

Design and development of smart sensors for rapid flame detection in postcombustion and early fire warning in precombustion situations are critically needed to improve the fire safety of combustible materials in many applications. Herein, we describe the fabrication of hierarchical coatings created by assembling a multilayered graphene oxide (GO)/silicone structure onto different combustible substrate materials. The resulting coatings exhibit distinct temperature-responsive electrical resistance change as efficient early warning sensors for detecting abnormal high environmental temperature, thus enabling fire prevention below the ignition temperature of combustible materials. After encountering a flame attack, we demonstrate extremely rapid flame detection response in 2-3 s and excellent flame self-extinguishing retardancy for the multilayered GO/silicone structure that can be synergistically transformed to a multiscale graphene/nanosilica protection layer. The hierarchical coatings developed are promising for fire prevention and protection applications in various critical fire risk and related perilous circumstances.

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Lung cancer is the leading cause of human cancer mortality due to the lack of early diagnosis technology. The low-dose computed tomography scan (LDCT) is one of the main techniques to screen cancers. However, LDCT still has a risk of radiation exposure and it is not suitable for the general public. In this study, plasma metabolic profiles of lung cancer were performed using a comprehensive metabolomic method with different liquid chromatography methods coupled with a Q-Exactive high-resolution mass spectrometer. Metabolites with different polarities (amino acids, fatty acids, and acylcarnitines) can be detected and identified as differential metabolites of lung cancer in small volumes of plasma. Logistic regression models were further developed to identify cancer stages and types using those significant biomarkers. Using the Variable Importance in Projection (VIP) and the area under the curve (AUC) scores, we have successfully identified the top 5, 10, and 20 metabolites that can be used to differentiate lung cancer stages and types. The discrimination accuracy and AUC score can be as high as 0.829 and 0.869 using the five most significant metabolites. This study demonstrated that using 5 + metabolites (Palmitic acid, Heptadecanoic acid, 4-Oxoproline, Tridecanoic acid, Ornithine, and etc.) has the potential for early lung cancer screening. This finding is useful for transferring the diagnostic technology onto a point-of-care device for lung cancer diagnosis and prognosis.