The sensitive nature of molecular hydrogen (H) interaction with the surfaces of pristine and functionalized nanostructures, especially two-dimensional materials, has been a subject of debate for a while now. An accurate approximation of the H adsorption mechanism has vital significance for fields such as H storage applications. Owing to the importance of this issue, we have performed a comprehensive density functional theory (DFT) study by means of several different approximations to investigate the structural, electronic, charge transfer and energy storage properties of pristine and functionalized graphdiyne (GDY) nanosheets. The dopants considered here include the light metals Li, Na, K, Ca, Sc and Ti, which have a uniform distribution over GDY even at high doping concentration due to their strong binding and charge transfer mechanism. Upon 11% of metal functionalization, GDY changes into a metallic state from being a small band-gap semiconductor. Such situations turn the dopants to a partial positive state, which is favorable for adsorption of H molecules. The adsorption mechanism of H on GDY has been studied and compared by different methods like generalized gradient approximation, van der Waals density functional and DFT-D3 functionals. It has been established that each functionalized system anchors multiple H molecules with adsorption energies that fall into a suitable range regardless of the functional used for approximations. A significantly high H storage capacity would guarantee that light metal-doped GDY nanosheets could serve as efficient and reversible H storage materials.

In this work, we have investigated the potential of pristine and silver (Ag)-functionalized graphene oxide monolayers GO (GO-Ag) as efficient membranes for water filtration. Our first principles calculations based on density functional theory (DFT) reveal the hydrophilic nature of GO surfaces. The phonon frequency calculations within density functional perturbation theory (DFPT) confirmed the stability of GO sheets in aqueous media. Van der Waals-corrected binding energies of GO sheet towards heavy metals suggest that even pristine GO sheets are completely impermeable to various heavy metals like arsenic (As) and lead (Pb). However, compared to GO, the GO-Ag sheets have a much higher affinity towards the three amino acids histidine, phenyl-alanine and tyrosine, which are the main component of a bacteria cell wall. The GO-Ag sheet is found to be extremely efficient for bacteria inactivation.

Glioblastoma (GBM) is the most common and aggressive stage IV brain cancer with a poor prognosis and survival rate. The blood–brain barrier (BBB) in GBM prevents the entry and exit of biomarkers, limiting its treatment options. Hence, GBM diagnosis is pivotal for timely clinical management. Currently, there exists no clinically validated biomarker for GBM diagnosis. T cells exhibit the potential to escape a leaky BBB in GBM patients. These T cells infiltrating the GBM interact with the heterogeneous population of tumor cells, display a symbiotic interaction resulting in intertwined molecular crosstalk, and display a GBM-associated signature while entering the peripheral circulation. Therefore, we hypothesize that studying these distinct molecular changes is critical to enable T cells to be a diagnostic marker for accurate detection of GBM from patient blood. We demonstrated this by utilizing the phenotypic and immunological landscape changes in T cells associated with glioblastoma tumors. GBM exhibits a high level of heterogeneity with diverse subtypes of cells within the tumor, enabling immune infiltration and different degrees of interactions with the tumor. To accurately detect these subtle molecular differences in T cells, we designed an immunosensor with a high detection sensitivity and repeatability. Hence in this study, we investigated the characteristic behavior of T cells to establish two preclinically validated biomarkers: GBM-associated T cells (GBMAT) and GBM stem cell-associated T cells (GSCAT). A comprehensive investigation was conducted by mimicking the tumor microenvironment in vitro by coculturing T cells with cancer cells and cancer stem cells to study the distinct variation in GBMAT and GSCAT. Preclinical investigation of T cells from GBM patient blood shows similar characteristics to our established biomarkers (GBMAT, GSCAT). Further evaluating the relative attributes of T cells in patient blood and tissue biopsy confirms the infiltrating ability of T cells across the BBB. A pilot validation using a SERS-based machine learning algorithm was accomplished by training the model with GBMAT and GSCAT as diagnostic markers. Using GBMAT as a biomarker, we achieved a sensitivity and specificity of 93.3% and 97.4%, respectively, whereas applying GSCAT yielded a sensitivity and specificity of 100% and 98.7%, respectively. We also validated this diagnostic methodology by using conventional biological assays to study the change in expression levels of T cell surface markers (CD4 and CD8) and cytokine levels in T cells (IL6, IL10, TNFα, INFγ) from GBM patients. This study introduces T cells as GBM-specific immune biomarkers to diagnose GBM using patient liquid biopsy. This preclinical validation study presents a better translatability into clinical reality that will enable rapid and noninvasive glioblastoma detection from patient blood.

The clinical relevance of liquid biopsy for glioblastoma (GBM) remains undetermined due to practical and biological limitations such as absence of a reliable GBM-specific biomarker, trace levels in circulation due to the blood-brain-barrier, and lack of a sensitive method to detect the trace levels of biomarkers. It is hypothesized that GBM stem cell (GSC)-associated cell free DNA can function as reliable biomarker for GBM because it accounts for tumor heterogeneity and provide accurate molecular information about the cancer. An integrative methodology is used for GBM diagnosis by utilizing the sub-single molecular sensitivity of nanoengineered plasmonic metasensors for real-time genomic profiling of GSC DNA. The nanoengineered metasensors can detect the rare circulating GSC-DNA accurately from just 5 µL of blood and the test can be performed in under 10 min. Analysis of clinical serum samples from GBM patients and healthy volunteers demonstrates that the technology yielded an accurate classification of GBM in an independent validation cohort (accuracy 98.3%, specificity 100%). The methodology detects GBM-signatures from the patient blood rapidly within the half-life period of cfDNA in circulation, noninvasively and amplification-free with a high diagnostic accuracy. With clinical validation, this methodology can evolve as a clinically viable diagnostic tool for fatal and hard-to-detect cancer like GBM.