In this work, different lanthanides (Tm³⁺, Er³⁺; Yb³⁺, Ho³⁺, Nd³⁺) were doped into NaYF₄ via a high-temperature coprecipitation method, and followed by SiO₂ coating to improve the water dispersibility, resulting in NaYF₄:Tm³⁺, Er³⁺@NaYF₄@SiO₂ and NaYF₄:Yb³⁺, Ho³⁺@NaYF₄:Nd³⁺@SiO₂ nanoparticles (NPs). The two NPs both exhibited the temperature-dependent second near-infrared (NIR-II) downshifting luminescence over the physiological range. The luminescence ratio of Tm³⁺ emission at 1460 nm to Er³⁺ emission at 1525 nm (Tm³⁺:³H₄ → ³F₄; Er³⁺:⁴I_13/2 → ⁴I_13/2) varies with temperature increase, as well as Yb³⁺ emission at 980 nm and Ho³⁺ emission at 1150 nm (Yb³⁺:²F_5/2 → ²F_7/2; Ho³⁺:⁵I₆ → ⁵I₈). The highest relative sensitivity of NaYF₄:Tm³⁺, Er³⁺@NaYF₄@SiO₂ and NaYF₄:Yb³⁺, Ho³⁺@NaYF₄:Nd³⁺@SiO₂ aqueous suspension is 0.36% K⁻¹ (at 298 K) and 0.76% K⁻¹ (at 343 K), respectively. The biological tests prove the good biocompatibility and low toxicity of the water-soluble NPs. In vitro tissue penetration experiments verify a much better penetration ability of the synthesized NaYF₄:Tm³⁺, Er³⁺@NaYF₄@SiO₂ compared with NaYF₄:Yb³⁺, Ho³⁺@NaYF₄:Nd³⁺@SiO₂ NPs. The excellent physiological luminescent thermometry with favor wave penetration depth provides a promising platform in deep tissue temperature measurement, which is very important in vivo biosensing.

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The deep reinforcement learning-based energy management strategies (EMS) have become a promising solution for hybrid electric vehicles (HEVs). When driving cycles are changed, the neural network will be retrained, which is a time-consuming and laborious task. A more efficient way of choosing EMS is to combine deep reinforcement learning (DRL) with transfer learning, which can transfer knowledge of one domain to the other new domain, making the network of the new domain reach convergence values quickly. Different exploration methods of DRL, including adding action space noise and parameter space noise, are compared against each other in the transfer learning process in this work. Results indicate that the network added parameter space noise is more stable and faster convergent than the others. In conclusion, the best exploration method for transferable EMS is to add noise in the parameter space, while the combination of action space noise and parameter space noise generally performs poorly. Our code is available at https://github.com/BIT-XJY/RL-based-Transferable-EMS.git.@en