Exploring the dual capabilities of BNQDs: a comprehensive study on enhancing photoelectric performance and photoluminescence via ligand functionalization.

CONTEXT: Boron nitride quantum dots (BNQDs) are emerging as promising multifunctional nanomaterials for renewable energy and optoelectronics owing to their versatile properties. However, rational design principles to tailor their photoelectric and photoluminescent capabilities remain scarce. This study employs density functional theory (DFT) to provide fundamental insights into using urea, thiourea, and PPD ligands to modulate the bandgap, charge transfer dynamics, and recombination processes of BNQDs. Modeling explains that incorporating specific ligands enables visible light absorption, spatial charge separation, continuous photocatalytic cycling, and high quantum yields in BNQDs. The structure-property relationships established pave the way for targeted synthesis of high-performance BNQD photocatalysts and light emitters. METHODS: This investigation utilized density functional theory (DFT) with the B3LYP functional and 6-31G(d,p) basis set to optimize the geometries of pristine and ligand-functionalized boron nitride quantum dots (BNQDs). The absorption spectra were generated using time-dependent DFT (TDDFT). A Ti38O76 cluster modeled the TiO2 substrate. The cpcm solvation model in Gaussian 09 defined the toluene solvent. Cohesive energies, charge transfer lengths, recombination rates, and conversion efficiencies were calculated to establish structure-property relationships. Multiwfn analyzed the charge densities. The modeling provides insights into tuning BNQD photocatalytic and photoluminescent properties using specific ligands.

The laws and effects of terahertz wave interactions with neurons.

Introduction: Terahertz waves lie within the energy range of hydrogen bonding and van der Waals forces. They can couple directly with proteins to excite non-linear resonance effects in proteins, and thus affect the structure of neurons. However, it remains unclear which terahertz radiation protocols modulate the structure of neurons. Furthermore, guidelines and methods for selecting terahertz radiation parameters are lacking. Methods: In this study, the propagation and thermal effects of 0.3-3 THz wave interactions with neurons were modelled, and the field strength and temperature variations were used as evaluation criteria. On this basis, we experimentally investigated the effects of cumulative radiation from terahertz waves on neuron structure. Results: The results show that the frequency and power of terahertz waves are the main factors influencing field strength and temperature in neurons, and that there is a positive correlation between them. Appropriate reductions in radiation power can mitigate the rise in temperature in the neurons, and can also be used in the form of pulsed waves, limiting the duration of a single radiation to the millisecond level. Short bursts of cumulative radiation can also be used. Broadband trace terahertz (0.1-2 THz, maximum radiated power 100 µW) with short duration cumulative radiation (3 min/day, 3 days) does not cause neuronal death. This radiation protocol can also promote the growth of neuronal cytosomes and protrusions. Discussion: This paper provides guidelines and methods for terahertz radiation parameter selection in the study of terahertz neurobiological effects. Additionally, it verifies that the short-duration cumulative radiation can modulate the structure of neurons.

Chlorophyll content for millet leaf using hyperspectral imaging and an attention-convolutional neural network / Estimativa do teor de clorofila para folhas de milheto usando imagens hiperespectrais e uma rede neural atenção-convolucional

ABSTRACT: Chlorophyll is a major factor affecting photosynthesis; and consequently, crop growth and yield. In this study, we devised a chlorophyll-content detection model for millet leaves in different stages of growth based on hyperspectral data. The hyperspectral images of millet leaves were obtained under a wavelength range of 380-1000 nm using a hyperspectral imager. Threshold segmentation was performed with near-infrared (NIR) reflectance and normalized difference vegetation index (NDVI) to intelligently acquire the regions of interest (ROI). Furthermore, raw spectral data were preprocessed using multivariate scatter correction (MSC). A correlation coefficient-successive projections algorithm (CC-SPA) was used to extract the characteristic wavelengths, and the characteristic parameters were extracted based on the spectral and image information. A partial least squares regression (PLSR) prediction model was established based on the single characteristic parameter and multi-characteristic parameter fusion. The determination coefficient (Rv 2) and the root-mean-square error (RMSEv) of the validation set for the multi-characteristic parameter fusion model were reported to be 0.813 and 1.766, respectively, which are higher than those obtained by the single characteristic parameter model. Based on the multi-characteristic parameter fusion, an attention-convolutional neural network (attention-CNN) (Rv 2 = 0.839, RMSEv = 1.451, RPD = 2.355) was established, which is more effective than the PLSR (Rv 2 = 0.813, RMSEv = 1.766, RPD = 2.167) and least squares support vector machine (LS-SVM) models (Rv 2 = 0.806, RMSEv = 1.576, RPD = 2.061). These results indicated that the combination of hyperspectral imaging and attention-CNN is beneficial to the application of nutrient element monitoring of crops.

RESUMO: A clorofila é um fator importante que afeta a fotossíntese e, consequentemente, o crescimento e o rendimento das culturas. Neste estudo, um modelo de detecção de conteúdo de clorofila é construído para folhas de milheto em diferentes estágios de crescimento, com base em dados hiperespectrais. As imagens hiperespectrais dos diferentes estágios de crescimento das folhas de milheto foram obtidas para 380-1000 nm, utilizando um gerador de imagens hiperespectrais. Uma segmentação de limiar foi realizada com refletância no infravermelho próximo (NIR) e índice de vegetação com diferença normalizada (NDVI) para adquirir de forma inteligente as regiões de interesse (ROI). Além disso, os dados espectrais brutos foram pré-processados usando o método de correção de dispersão multivariada (MSC). Um algoritmo de projeção de coeficiente de correlação sucessivo (CC-SPA) foi utilizado para extrair os comprimentos de onda característicos, e os parâmetros característicos foram extraídos com base nas informações espectrais e de imagem. O modelo de previsão de regressão parcial dos mínimos quadrados (PLSR) foi estabelecido com base nos parâmetros de característica única e na fusão de parâmetros de característica múltipla. O coeficiente de determinação (Rv2) e o erro quadrático médio da raiz (RMSEv) do conjunto de validação para o modelo de fusão de parâmetros com várias características foram obtidos como 0,813 e 1,766, sendo melhores do que os do modelo de parâmetro de característica única. Com base na fusão de parâmetros com várias características, foi estabelecida uma rede neural atenção-convolucional (atenção-CNN) (Rv2 = 0,839, RMSEv = 1,451, RPD = 2,355) mais eficaz que o PLSR (Rv2 = 0,813, RMSEv = 1,766, RPD = 2,167) e mínimos quadrados que suportam modelos de máquina de vetores (LS-SVM) (Rv2 = 0,806, RMSEv = 1,576, RPD = 2,061). Estes resultados indicam que o modelo atenção-CNN atinge uma previsão efetiva do teor de clorofila nas folhas de milheto usando os dados hiperespectrais. Além disso, esta pesquisa demonstra que a combinação de imagens hiperespectrais e a atenção-CNN se mostra benéfica para a aplicação do monitoramento dos elementos nutricionais das culturas.

Fabrication of polycrystalline tubular ZnO via a modified ultrasonically assisted two-step polyol process and characterization of the nanotubes.

An ultrasonically assisted two-step polyol process was established to fabricate polycrystalline ZnO nanotubes. Thus one-dimensional (1D) precursors were prepared from an ethylene glycol (EG) solution containing 0.3 M of zinc acetate in the presence of ultrasonic irradiation. The ZnO nanotubes were obtained by calcination of the precursors at proper temperatures. The precursors and polycrystalline ZnO nanotubes obtained at various calcination temperatures were characterized by means of scanning electron microscopy (SEM), Fourier transformation infrared spectrometry (FTIR), x-ray diffraction (XRD, together with temperature-resolved XRD), and transmission electron microscopy (TEM). It was found that the precursors were extremely sensitive to atmospheric moisture and instantly transformed to layered hydroxide zinc acetate (LHS-Zn) after being exposed to air, accompanied by the erosion and deformation of the one-dimensional structure. After being calcined at proper temperatures, the precursors were completely transformed into polycrystalline tubular ZnO, and the sizes of the resulting ZnO nanocrystallites increased with increasing calcination temperature, implying that polycrystalline tubular ZnO of desired sizes could be fabricated using the present method by properly controlling the calcination temperature. However, the tubular structures were destroyed at a calcination temperature of 400 °C and above, owing to the growth of polycrystalline ZnO. Moreover, the present method could be used to synthesize other tubular metal oxides, and tubular ZnO might find promising applications in gas-sensitive sensors and catalysis as well.