Lymphoma is a significant cancer that affects the human lymphatic and hematopoietic systems. In this work, discrimination of lymphoma using laser-induced breakdown spectroscopy (LIBS) conducted on whole blood samples is presented. The whole blood samples collected from lymphoma patients and healthy controls are deposited onto standard quantitative filter papers and ablated with a 1064 nm Q-switched Nd:YAG laser. 16 atomic and ionic emission lines of calcium (Ca), iron (Fe), magnesium (Mg), potassium (K) and sodium (Na) are selected to discriminate the cancer disease. Chemometric methods, including principal component analysis (PCA), linear discriminant analysis (LDA) classification, and k nearest neighbor (kNN) classification are used to build the discrimination models. Both LDA and kNN models have achieved very good discrimination performances for lymphoma, with an accuracy of over 99.7%, a sensitivity of over 0.996, and a specificity of over 0.997. These results demonstrate that the whole-blood-based LIBS technique in combination with chemometric methods can serve as a fast, less invasive, and accurate method for detection and discrimination of human malignancies.

Diagnosis and staging of multiple myeloma (MM) have been achieved using serum-based laser-induced breakdown spectroscopy (LIBS) in combination with machine learning methods. 130 cases of serum samples collected from registered MM patients in different progressive stages and healthy controls were deposited onto standard quantitative filter papers and ablated with a Q-switched Nd:YAG laser. Emissions of Ca, Na, K, Mg, C, H, O, N and CN were selected for malignancy diagnosis and staging. Multivariate statistics and machine learning methods, including principal component analysis (PCA), k-nearest neighbor (kNN), support vector machine (SVM) and artificial neural network (ANN) classifiers, were used to build the discrimination models. The performances of the classifiers were optimized via 10-fold cross-validation and evaluated in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curves (AUC). The kNN, SVM and ANN classifiers achieved comparable discrimination performances with accuracies of over 90% for both diagnosis and staging of MM. For diagnosis of MM, the classifiers achieved performances with AUC of ∼0.970, sensitivity of ∼0.930 and specificity of ∼0.910; for staging of MM, the corresponding values were AUC of ∼0.970, sensitivity of ∼0.910 and specificity of ∼0.930. These results show that the serum-based LIBS in combination with machine learning methods can serve as a fast, less invasive, cost-effective, and robust technique for diagnosis and staging of human malignancies.

We have for the first time achieved cavity ignition and sustainable combustion of liquid kerosene in supersonic flow of Mach number 2.52 using a laser-induced plasma (LIP) on a model supersonic combustor equipped with dual cavities in tandem as flameholders. The liquid kerosene of ambient temperature is injected from the front wall of the upstream cavity, while the ignitions have been conducted in both cavities. High-speed chemiluminescence imaging shows that the flame kernel initiated in the downstream cavity can propagate contraflow into upstream cavity and establish full sustainable combustion. Based on the qualitative distribution of the kerosene vapor in the cavity, obtained using the kerosene planar laser-induced fluorescence technique, we find that the fuel atomization and evaporation, local hydrodynamic and mixing conditions in the vicinity of the ignition position and in the leading edge area of the cavity have combined effects on the flame kernel evolution and the eventual ignition results.

Serum contains rich elemental information, and the elemental analysis of the serum using laser-induced breakdown spectroscopy (LIBS) can help with the diagnosis and treatment of cancer. In this work, serum specimens from breast cancer patients were used as samples, and the elements Mg, Ca, and K were analyzed using LIBS. Silver nanoparticles (Ag NPs) were introduced to enhance spectral emission intensities, and this technique is known as nanoparticle-enhanced LIBS (NELIBS). An Ag NP-coated filter paper (Ag NP paper) substrate was prepared and was compared with silicon and aluminum substrates for NELIBS analysis. The Ag NP paper substrate showed lower spectral intensity variances and better elemental distribution uniformity due to the reduced coffee-ring effect compared with silicon and aluminum substrates. The enhanced effect of the Ag NP paper was optimized by adjusting serum volume and Ag NPs concentration. It was found that Mg, Ca, and K required different optimal conditions for spectral enhancement. The highest enhancement factors (EF) for Mg, Ca, and K were 1.85, 1.76, and 3.10, respectively. The electron temperature (T_e) and electron number density (N_e) of NELIBS plasmas were measured using the Boltzmann plot and Stark broadening methods and compared with those of the normal LIBS plasmas. The T_e and N_e of the NELIBS and normal LIBS samples were generally comparable with each other. Higher Ag NP concentrations led to lower T_e values for NELIBS plasmas.