Significantly Enhanced Thermoelectric Performance of γ-In2Se3 through Lithiation via Chemical Diffusion

γ-In2Se3 is selected as a thermoelectric candidate because it has a unique crystal structure and thermal stability at relatively high temperatures. In this work we have prepared lithiated γ-In2Se3 through chemical diffusion and investigated its band structures and thermoelectric performance. After lithiation of γ-In2Se3 in lithium acetate (CH3COOLi) solution at 50oC, we have observed a high Hall carrier concentration (nH) up to ≤1.71×1018 cm-3 at room temperature (RT), which is about ∼4 orders of magnitude compared to that of pristine γ-In2Se3. The enhancement in nH is directly responsible for the remarkable improvement in electrical conductivity, and can be elucidated as the Fermi level (Fr) unpinning and moving towards the conduction band (CB) through the dominant interstitial occupation of Li+ in the γ-In2Se3 lattice. Combined with the minimum lattice thermal conductivity (κL=0.30-0.34 WK-1m-1) at ~923 K, the highest ZT value of 0.62-0.67 is attained, which is about 9-10 times that of pristine γ-In2Se3, proving that the lithiation in γ-In2Se3 is an effective approach on the improvement of the thermoelectric performance

High-Speed Photothermal Patterning of Doped Polymer Films.

Organic semiconductors (OSCs) offer a new avenue to the next-generation electronics, but the lack of a scalable and inexpensive nanoscale patterning/deposition technique still limits their use in electronic applications. Recently, a new lithographic etching technique has been introduced that uses molecular dopants to reduce semiconducting polymer solubility in solvents and a direct-write laser to remove dopants locally, enabling rapid OSC etching with diffraction limited resolution. Previous publications postulated that the reaction that enables patterning is a photochemical reaction between photoexcited dopants with neutral solvent molecules. In this work, we analyze the photoinduced dissolution kinetics of F4TCNQ doped P3HT films using time-resolved in situ optical probing. We find two competing mechanisms that control de-doping and dissolution: the first is the photochemical reaction posited in the literature, and the second involves direct heating of the polymer by the laser, inducing increased solubility for both the polymer and dopant. We show that the wavelength-specific photochemical effect is dominant in low photon doses while the photothermal effect is dominant with high excitation rates regardless of laser wavelength. With sufficiently high optical intensity input, the photothermal mechanism can in principle achieve a high writing speed up to 1 m/s. Our findings bring new insights into the mechanisms behind laser direct writing of OSCs based on dopant induced solubility control and enable ultraprecise fabrications of various device configurations in large-scale manufacturing

MediViSTA-SAM: Zero-shot Medical Video Analysis with Spatio-temporal SAM Adaptation

In recent years, the Segmentation Anything Model (SAM) has attracted considerable attention as a foundational model well-known for its robust generalization capabilities across various downstream tasks. However, SAM does not exhibit satisfactory performance in the realm of medical image analysis. In this study, we introduce the first study on adapting SAM on video segmentation, called MediViSTA-SAM, a novel approach designed for medical video segmentation. Given video data, MediViSTA, spatio-temporal adapter captures long and short range temporal attention with cross-frame attention mechanism effectively constraining it to consider the immediately preceding video frame as a reference, while also considering spatial information effectively. Additionally, it incorporates multi-scale fusion by employing a U-shaped encoder and a modified mask decoder to handle objects of varying sizes. To evaluate our approach, extensive experiments were conducted using state-of-the-art (SOTA) methods, assessing its generalization abilities on multi-vendor in-house echocardiography datasets. The results highlight the accuracy and effectiveness of our network in medical video segmentation

Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities

Recent advances in artificial general intelligence (AGI), particularly large language models and creative image generation systems have demonstrated impressive capabilities on diverse tasks spanning the arts and humanities. However, the swift evolution of AGI has also raised critical questions about its responsible deployment in these culturally significant domains traditionally seen as profoundly human. This paper provides a comprehensive analysis of the applications and implications of AGI for text, graphics, audio, and video pertaining to arts and the humanities. We survey cutting-edge systems and their usage in areas ranging from poetry to history, marketing to film, and communication to classical art. We outline substantial concerns pertaining to factuality, toxicity, biases, and public safety in AGI systems, and propose mitigation strategies. The paper argues for multi-stakeholder collaboration to ensure AGI promotes creativity, knowledge, and cultural values without undermining truth or human dignity. Our timely contribution summarizes a rapidly developing field, highlighting promising directions while advocating for responsible progress centering on human flourishing. The analysis lays the groundwork for further research on aligning AGI's technological capacities with enduring social goods

Experimental demonstration of a free space cylindrical cloak without superluminal propagation

We experimentally demonstrated an alternative approach of invisibility cloaking that can combine technical advantages of all current major cloaking strategies in a unified manner and thus can solve bottlenecks of individual strategies. A broadband cylindrical invisibility cloak in free space is designed based on scattering cancellation (the approach of previous plasmonic cloaking), and implemented with anisotropic metamaterials (a fundamental property of singular-transformation cloaks). Particularly, non-superluminal propagation of electromagnetic waves, a superior advantage of non-Euclidian-transformation cloaks constructed with complex branch cuts, is inherited in this design, and thus is the reason of its relatively broad bandwidth. This demonstration provides the possibility for future practical implementation of cloaking devices at large scales in free space.Comment: 16 pages, 3 figures, accepted by Physical Review Letter

High photocatalytic activity of Cu2O embedded in hierarchically hollow SiO2 for efficient chemoselective hydrogenation of nitroarenes

Photocatalytic organic conversion is a crucial process in the hydrogenation of nitroarenes, but harsh reaction conditions such as long reaction time, high hydrogen pressure, and organic medium still need to be considerably overcome under visible-light irradiation. Here, we have constructed a transition metal oxide photocatalyst by embedding low-cost Cu2O with strong visible-light absorption into hierarchically hollow SiO2 sphere (SiO2-Cu2O@SiO2) that can suppress the escape of photogenerated atomic hydrogen and promote the contact probability between hydrogen atom and nitroarene molecules due to confinement effect. Remarkably, the SiO2-Cu2O@SiO2 photocatalyst can exhibit efficient chemoselectivity toward the hydrogenation of various nitroarenes in an aqueous system at ambient conditions, successfully working out the requirement of strict hydrogenation conditions, especially for organic medium over almost all of the reported photocatalysts. Notably, quantitative aniline can be produced for the visible-light catalytic reduction of nitroarenes, suggesting a considerable potential for industrial applicatio

PharmacyGPT: The AI Pharmacist

In this study, we introduce PharmacyGPT, a novel framework to assess the capabilities of large language models (LLMs) such as ChatGPT and GPT-4 in emulating the role of clinical pharmacists. Our methodology encompasses the utilization of LLMs to generate comprehensible patient clusters, formulate medication plans, and forecast patient outcomes. We conduct our investigation using real data acquired from the intensive care unit (ICU) at the University of North Carolina Chapel Hill (UNC) Hospital. Our analysis offers valuable insights into the potential applications and limitations of LLMs in the field of clinical pharmacy, with implications for both patient care and the development of future AI-driven healthcare solutions. By evaluating the performance of PharmacyGPT, we aim to contribute to the ongoing discourse surrounding the integration of artificial intelligence in healthcare settings, ultimately promoting the responsible and efficacious use of such technologies

MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation

The Segment Anything Model (SAM), a foundation model for general image segmentation, has demonstrated impressive zero-shot performance across numerous natural image segmentation tasks. However, SAM's performance significantly declines when applied to medical images, primarily due to the substantial disparity between natural and medical image domains. To effectively adapt SAM to medical images, it is important to incorporate critical third-dimensional information, i.e., volumetric or temporal knowledge, during fine-tuning. Simultaneously, we aim to harness SAM's pre-trained weights within its original 2D backbone to the fullest extent. In this paper, we introduce a modality-agnostic SAM adaptation framework, named as MA-SAM, that is applicable to various volumetric and video medical data. Our method roots in the parameter-efficient fine-tuning strategy to update only a small portion of weight increments while preserving the majority of SAM's pre-trained weights. By injecting a series of 3D adapters into the transformer blocks of the image encoder, our method enables the pre-trained 2D backbone to extract third-dimensional information from input data. The effectiveness of our method has been comprehensively evaluated on four medical image segmentation tasks, by using 10 public datasets across CT, MRI, and surgical video data. Remarkably, without using any prompt, our method consistently outperforms various state-of-the-art 3D approaches, surpassing nnU-Net by 0.9%, 2.6%, and 9.9% in Dice for CT multi-organ segmentation, MRI prostate segmentation, and surgical scene segmentation respectively. Our model also demonstrates strong generalization, and excels in challenging tumor segmentation when prompts are used. Our code is available at: https://github.com/cchen-cc/MA-SAM