Ultralow-Frequency Tip-Enhanced Raman Scattering Discovers Nanoscale Radial Breathing Mode on Strained 2D Semiconductors.

Collective excitations including plasmons, magnons, and layer-breathing vibration modes emerge at an ultralow frequency (<1 THz) and are crucial for understanding van der Waals materials. Strain at the nanoscale can drastically change the property of van der Waals materials and create localized states like quantum emitters. However, it remains unclear how nanoscale strain changes collective excitations. Herein, ultralow-frequency tip-enhanced Raman spectroscopy (TERS) with sub-10 nm resolution under ambient conditions is developed to explore the localized collective excitation on monolayer semiconductors with nanoscale strains. A new vibrational mode is discovered at around 12 cm-1 (0.36 THz) on monolayer MoSe2 nanobubbles and it is identified as the radial breathing mode (RBM) of the curved monolayer. The correlation is determined between the RBM frequency and the strain by simultaneously performing deterministic nanoindentation and TERS measurement on monolayer MoSe2. The generality of the RBM in nanoscale curved monolayer WSe2 and bilayer MoSe2 is demonstrated. Using the RBM frequency, the strain of the monolayer MoSe2 on the nanoscale can be mapped. Such an ultralow-frequency vibration from curved van der Waals materials provides a new approach to study nanoscale strains and points to more localized collective excitations to be discovered at the nanoscale.

Signal2signal: Pushing the Spatiotemporal Resolution to the Limit by Single Chemical Hyperspectral Imaging.

There is growing interest in developing a high-performance self-supervised denoising algorithm for real-time chemical hyperspectral imaging. With a good understanding of the working function of the zero-shot Noise2Noise-based denoising algorithm, we developed a self-supervised Signal2Signal (S2S) algorithm for real-time denoising with a single chemical hyperspectral image. Owing to the accurate distinction and capture of the weak signal from the random fluctuating noise, S2S displays excellent denoising performance, even for the hyperspectral image with a spectral signal-to-noise ratio (SNR) as low as 1.12. Under this condition, both the image clarity and the spatial resolution could be significantly improved and present an almost identical pattern with a spectral SNR of 7.87. The feasibility of real-time denoising during imaging was well demonstrated, and S2S was applied to monitor the photoinduced exfoliation of transition metal dichalcogenide, which is hard to accomplish by confocal Raman spectroscopy. In general, the real-time denoising capability of S2S offers an easy way toward in situ/in vivo/operando research with much improved spatial and temporal resolution. S2S is open-source at https://github.com/3331822w/Signal2signal and will be accessible online at https://ramancloud.xmu.edu.cn/tutorial.

Noise learning of instruments for high-contrast, high-resolution and fast hyperspectral microscopy and nanoscopy.

The low scattering efficiency of Raman scattering makes it challenging to simultaneously achieve good signal-to-noise ratio (SNR), high imaging speed, and adequate spatial and spectral resolutions. Here, we report a noise learning (NL) approach that estimates the intrinsic noise distribution of each instrument by statistically learning the noise in the pixel-spatial frequency domain. The estimated noise is then removed from the noisy spectra. This enhances the SNR by ca. 10 folds, and suppresses the mean-square error by almost 150 folds. NL allows us to improve the positioning accuracy and spatial resolution and largely eliminates the impact of thermal drift on tip-enhanced Raman spectroscopic nanoimaging. NL is also applicable to enhance SNR in fluorescence and photoluminescence imaging. Our method manages the ground truth spectra and the instrumental noise simultaneously within the training dataset, which bypasses the tedious labelling of huge dataset required in conventional deep learning, potentially shifting deep learning from sample-dependent to instrument-dependent.

Soluble Nanographene C222: Synthesis and Applications for Synergistic Photodynamic/Photothermal Therapy.

Nanographene C222, which consists of a planar graphenic plane containing 222 carbon atoms, holds the record as the largest planar nanographene synthesized to date. However, its complete insolubility makes the processing of C222 difficult. Here we addressed this issue by introducing peripheral substituents perpendicular to the graphene plane, effectively disrupting the interlayer stacking and endowing C222 with good solubility. We also found that the electron-withdrawing substituents played a crucial role in the cyclodehydrogenation process, converting the dendritic polyphenylene precursor to C222. After disrupting the interlayer stacking, the introduction of only a few peripheral carboxylic groups allowed C222 to dissolve in phosphate buffer saline, reaching a concentration of up to 0.5 mg/mL. Taking advantage of the good photosensitizing and photothermal properties of the inner C222 core, the resulting water-soluble C222 emerged as a single-component agent for both photothermal and photodynamic tumor therapy, exhibiting an impressive tumor inhibition rate of 96%.