Dibyendu Mukherjee

Single-/double-walled carbon nanotubes (SWCNTs/DWCNTs) are increasingly sought-after as functional additives in polymer/ceramic composites to enhance their mechanical strength, electromagnetic shielding, and heating/curing properties. Traditional gas-phase syntheses of CNTs require metal-based catalysts and growth substrates; yet their scalability remains limited by weak material recoverability due to wall losses, post-synthesis catalyst removal, and substrate-attached growth that prevent direct dispersion into polymers or active solvents. We present Laser Ablation Synthesis in Solution (LASiS) as a robust, facile, and versatile method for producing colloidal CNTs with significant yield and purity without extraneous catalysts. Using graphite as the ablation target under toluene and p‑xylene as organic solvents, we selectively synthesize SWCNTs and DWCNTs respectively. High-resolution imaging and detailed materials characterizations reveal high density of CNTs with uniform aspect ratios, that include SWCNTs with a median diameter of 1.4 nm and a median length of 31.35 nm, and DWCNTs with a total diameter centered near 5.0 nm, a wall-count proxy of ~2.09, and a median length of 573.14 nm that indicate the characteristic structural motifs of SWCNTs and DWCNTs. Furthermore, we investigate their optical absorption and band gap properties to study their optoelectronic interactions. Post-processing moderate temperature treatments also indicate further improvement in their purity. Our results indicate that solvent properties and laser parameters influence the yield, purity, morphology and control on the formation of SWCNTs and DWCNTs. We discuss the scalability and limitations of LASiS in improving the purity, chirality, and structural integrity of CNTs produced directly without using metal catalysts and complex set-ups.

X-ray PDF tracks disorder-to-order evolution in oxygen-rich metastable AlO _x /C nanocomposites; laser flux (fluence/pulse duration) governs O/Al in metastable AlO _x /C nanocomposites.

LASiS-based HNCs of nanostructured MnO_x/Mn₃O₄.

Analytical advantages of facile and expeditious spectral data collections from laser-induced breakdown spectroscopy (LIBS) are often offset by the low-accuracy quantitative analyses offered by the technique due to non-equilibrium plasma–matrix interactions. Herein, we developed a one-dimensional (1D) convolutional neural network (CNN) and a least absolute shrinkage and selection operator (LASSO) models for LIBS data analyses to predict trace amounts of interstitial oxygen impurities in commercial Czochralski-silicon (Cz-Si) crystals with known interstitial oxygen concentrations at 0–16 parts per million (ppm). While traditional spectral analyses from O(I) (777.2 nm) atomic lines offer poor accuracy, CNN and LASSO analyses generate excellent predictions for the interstitial oxygen concentrations. Specifically, CNN-based spectral analyses uniquely identified systematic alterations in LIBS fingerprints manifested by laser-matter interactions. Our results pave the path for combining facile and voluminous LIBS data collection with deep learning driven high-fidelity data analytics.