Charge Behavior of Terminal Hydroxyl on Rutile TiO2(110)

Designing a highly near infrared-reflective black nanoparticles for autonomous driving based on the refractive index and principle

HYPOTHESIS

The development of highly NIR reflective black single-shell hollow nanoparticles (BSS-HNPs) can overcome the Light Detection and Ranging (LiDAR) sensor limitations of dark-tone materials. The crystalline phase of TiO2 and the refractive index can be controlled by calcination temperature. The formation of hollow structure and the refractive index is expected to simultaneously increase the light reflection and LiDAR detectability.

EXPERIMENTS

The BSS-HNPs are synthesized using the sol-gel method, calcination, NaBH4 reduction, and etching to form a hollow structure with true blackness. The computational bandgap calculation is conducted to determine the bandgap energy (Eg) of the white and black TiO2 with different crystalline structures. The blackness of the as-synthesized materials is determined by the Commission on Illumination (CIE) L*a*b* color system.

FINDINGS

The hydrophilic nature of BSS-HNPs enables the formulation of hydrophilic paints, allowing the mono-layer coating. With the synergistic effects of hollow structure and the refractive index, BSS-HNPs manifested superb NIR reflectance at LiDAR detection wavelengths. The high detectability, blackness, and hollow structure of BSS-HNPs can expand the variety of LiDAR-detectable dark-tone materials.

The multivariate interaction between Au and TiO2 colloids: the role of surface potential, concentration, and defects

The established DLVO theory explains colloidal stability by the electrostatic repulsion between electrical double layers. While the routinely measured zeta potential can estimate the charges of double layers, it is only an average surface property which might deviate from the local environment. Moreover, other factors such as the ionic strength and the presence of defects should also be considered. To investigate this multivariate problem, here we model the interaction between a negatively charged Au particle and a negatively charged TiO2 surface containing positive/neutral defects (e.g. surface hydroxyls) based on the finite element method, over 6000 conditions of these 6 parameters: VPart (particle potential), VSurf (surface potential), VDef (defect potential), DD (defect density), Conc (salt concentration), and R (particle radius). Using logistic regression, the relative importance of these factors is determined: VSurf > VPart > DD > Conc > R > VDef, which agrees with the conventional wisdom that the surface (and zeta) potential is indeed the most decisive descriptor for colloidal interactions, and the salt concentration is also important for charge screening. However, when defects are present, it appears that their density is more influential than their potential. To predict the fate of interactions more confidently with all the factors, we train a support vector machine (SVM) with the simulation data, which achieves 97% accuracy in determining whether adsorption is favorable on the support. The trained SVM including a graphical user interface for querying the prediction is freely available online for comparing with other materials and models. We anticipate that our model can stimulate further colloidal studies examining the importance of the local environment, while simultaneously considering multiple factors.

Direct measurement of surface photovoltage by AC bias Kelvin probe force microscopy

Surface photovoltage (SPV) measurements are a crucial way of investigating optoelectronic and photocatalytic semiconductors. The local SPV is generally measured consecutively by Kelvin probe force microscopy (KPFM) in darkness and under illumination, in which thermal drift degrades spatial and energy resolutions. In this study, we propose the method of AC bias Kelvin probe force microscopy (AC-KPFM), which controls the AC bias to nullify the modulated signal. We succeeded in directly measuring the local SPV by AC-KPFM with higher resolution, thanks to the exclusion of the thermal drift. We found that AC-KPFM can achieve a SPV response faster by about one to eight orders of magnitude than classical KPFM. Moreover, AC-KPFM is applicable in both amplitude modulation and frequency modulation mode. Thus, it contributes to advancing SPV measurements in various environments, such as vacuum, air, and liquids. This method can be utilized for direct measurements of changes in surface potential induced by modulated external disturbances.