Intermolecular hydrogen-bonding as a robust tool toward significantly improving the photothermal conversion efficiency of a NIR-II squaraine dye

The photothermal therapy(PTT) has come across as a promising noninvasive therapeutic strategy for tumor treatment. However, low photothermal conversion efficiency(PCE) and hydrophobicity may impede the therapeutic efficacy of organic photothermal agents and an efficient PTT-agent must overcome these two major challenges. In this work, we developed a new strategy to promote higher PCE wherein the intermolecular hydrogen-bonding interaction between the single dye molecule and water facilitated the transformation of the absorbed energy into the heat. A hydrophilic squaraine dye(SCy1) with the second near-infrared region(NIR-II) absorption and extremely low emission were designed to exhibit much higher PCE than that of the analogues of pentamethine-dyes(PCy1, PCy2). The presence of the ‘–O-' at middle of squaric cycle enabled the intermolecular H-bonding formation between the SCy1 and water to promote the energy dissipation channel. Moreover, the introduction of long-chain phenylsulfonate groups helped in to improve the water solubility apart from serving as an additional means of further enhancing PCE through fluorescence quenching. Therefore, SCy1 with a squaraine backbone and long-chain sulfonate moieties revealed outstanding photothermal stability and anti-aggregation activity apart from showing exceptionally high PCE(74%) in water. SCy1 demonstrated excellent therapeutic efficacy when applied in the PTT treatment of tumor-bearing mice under a laser irradiation of 915 nm.

Non-specific/specific SERS spectra concatenation for precise bacteria classifications with few samples using a residual neural network

Deep learning neural network incorporating surface enhancement Raman scattering technique(SERS)is becoming as a powerful tool for the precise classifications and diagnosis of bacterial infections.However,the large amount of sample requirement and time-consuming sample collection severely hinder its applications.We herein propose a spectral concatenation strategy for residual neural network using nonspecific and specific SERS spectra for the training data augmentation,which is accessible to acquiring larger training dataset with same number of SERS spectra or same size of training dataset with fewer SERS spectra,compared with pure non-specific SERS spectra.With this strategy,the training loss exhibit rapid convergence,and an average accuracy up to 100%in bacteria classifications was achieved with50 SERS spectra for each kind of bacterium;even reduced to 20 SERS spectra per kind of bacterium,classification accuracy is still>95%,demonstrating marked advantage over the results without spectra concatenation.This method can markedly improve the classification accuracy under fewer samples and reduce the data collection workload,and can evidently enhance the performance when used in different machine learning models with high generalization ability.Therefore,this strategy is beneficial for rapid and accurate bacteria classifications with residual neural network.