hampered to date by the limited success in the development of clinical photothermal therapy agents. [6] PTT exploits heat generated locally by a photosensitizer, [7] in which both the photothermal agents and laser sources, as well as the matching between them, are essential. Laser thermal therapy generally employs continuouswave lasers with wavelengths of either 808 or 980 nm. [8,9] The wavelengths are in the near-IR (NIR, λ = 700-1100 nm) window so that photons can penetrate deep into biological tissue. [10][11][12] Certain nanoparticles (NPs) have proper carrier densities enabling them to exhibit localized surface plasmon resonances (LSPRs) that efficiently facilitate the conversion of NIR light into heat. [10][11][12] Several types of NPs are currently being developed as photosensitizers, including metallic and semiconductor NPs. Noble metal NPs, for example, Ag and Au NPs, have been extensively applied for the LSPRs in the visible spectrum. [5,[13][14][15] Semi conductor NPs have tunable carrier concentration and LSPRs typically in the NIR range. [10,11] A high photothermal conversion efficiency is key to effective NP photosensitizers to avoid thermal damage to healthy tissue, which is a serious problem in PTT. The NPs with a high photothermal conversion efficiency and tumor selectivity can thus effectively destroy the cancer cells at a low photon density and in a short treatment time, while keeping the surrounding healthy tissue at a safe temperature. [16] Other key factors to consider for developing Photothermal therapy requires efficient plasmonic nanomaterials with small size, good water dispersibility, and biocompatibility. This work reports a one-pot, 2-min synthesis strategy for ultrathin CuS nanocrystals (NCs) with precisely tunable size and localized surface plasmon resonance (LSPR), where a single-starch-layer coating leads to a high LSPR absorption at the near-IR wavelength 980 nm. The CuS NC diameter increases from 4.7 (1 nm height along [101]) to 28.6 nm (4.9 nm height along [001]) accompanied by LSPR redshift from 978 to 1200 nm, as the precursor ratio decreases from 1 to 0.125. Photothermal temperature increases by 38.6 °C in 50 mg L −1 CuS NC solution under laser illumination (980 nm, 1.44 W cm −2 ). Notably, 98.4% of human prostate cancer PC-3/Luc+ cells are killed by as little as 5 mg L −1 starch-coated CuS NCs with 3-min laser treatment, whereas CuS NCs without starch cause insignificant cell death. LSPR modeling discloses that the starch layer enhances the photothermal effect by significantly increasing the free carrier density and blue-shifting the LSPR toward 980 nm. This study not only presents a new type of photothermally highly efficient ultrathin CuS NCs, but also offers in-depth LSPR modeling investigations useful for other photothermal nanomaterial designs.

Trajectory-based spatiotemporal entity linking is to match the same moving object in different datasets based on their movement traces. It is a fundamental step to support spatiotemporal data integration and analysis. In this paper, we study the problem of spatiotemporal entity linking using effective and concise signatures extracted from their trajectories. This linking problem is formalized as a k-nearest neighbor (k-NN) query on the signatures. Four representation strategies (sequential, temporal, spatial, and spatiotemporal) and two quantitative criteria (commonality and unicity) are investigated for signature construction. A simple yet effective dimension reduction strategy is developed together with a novel indexing structure called the WR-tree to speed up the search. A number of optimization methods are proposed to improve the accuracy and robustness of the linking. Our extensive experiments on real-world datasets verify the superiority of our approach over the state-of-the-art solutions in terms of both accuracy and efficiency.

Understanding and predicting morphing response of printed active structures remain a challenge in 4D printing. To tackle it, in this paper, we present a consolidated data-driven approach enabled by an ensemble of machine learning (ML) algorithms. First, three ML algorithms were employed to quantitatively correlate a geometrical feature (thickness) with the final morphing shapes indicated by curvatures and curving angles. Among them, the gradient boosting algorithm achieved correlation factors (R 2) of 0.96 and 0.94 when predicting the curvatures and curving angles by using the data collected from 150 experiments. The random forest model enabled to rank the importance of fabrication parameters in determining the shape morphing behaviors. To forecast the dynamic response of printed structures, three time series forecast algorithms were implemented based on the time-dependent image data during morphing processes of the printed active structures. Among them, the exponential smoothing method achieved an average mean absolute percentage error of 0.0139. This work offers a proof-of-concept on how the ensemble ML algorithms can be employed to delineate and predict morphing mechanism of printed active structures, thus providing a new paradigm for advancing the state-of-the-art research in 4D printing.

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