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Selective metallization is widely used in the fabrication of conductive circuits and metallized patterns. In this work, a facile approach for the fabrication of copper patterns on polydimethylsiloxane (PDMS) substrates was developed through a 1064 nm pulsed near-infrared (NIR) laser activation and selective metallization. The laser sensitizer of copper hydroxyl phosphate [Cu 2 (OH)PO 4 ] and antimony-doped tin oxide (ATO) were incorporated into PDMS resin to prepared laser direct structuring (LDS) materials, respectively. The physical morphology and chemical composition of PDMS/Cu 2 (OH)PO 4 and PDMS/ATO after 1064 nm pulsed NIR laser activation and metallization characterizations were determined. The laser-irradiated area of the PDMS substrate exhibited catalyst activation, which could trigger an electroless copper plating (ECP) reaction. The obtained copper patterns exhibit strong mechanical adhesion on the PDMS substrate, which reaches class 5B level after an adhesive tape test (ASTM D3359 standard). Moreover, the conductivity of the obtained copper pattern on PDMS/ Cu 2 (OH)PO 4 and PDMS/ATO is around 2.09 × 10 7 and 1.38 × 10 7 Ω −1 •m −1 . This study provides a strategy to fabricate PDMSbased LDS materials for large-scale industrial applications.

We study the problem of safe offline reinforcement learning (RL), the goal is to learn a policy that maximizes long-term reward while satisfying safety constraints given only offline data, without further interaction with the environment. This problem is more appealing for real world RL applications, in which data collection is costly or dangerous. Enforcing constraint satisfaction is non-trivial, especially in offline settings, as there is a potential large discrepancy between the policy distribution and the data distribution, causing errors in estimating the value of safety constraints. We show that naïve approaches that combine techniques from safe RL and offline RL can only learn sub-optimal solutions. We thus develop a simple yet effective algorithm, Constraints Penalized Q-Learning (CPQ), to solve the problem. Our method admits the use of data generated by mixed behavior policies. We present a theoretical analysis and demonstrate empirically that our approach can learn robustly across a variety of benchmark control tasks, outperforming several baselines.

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Physiological signals can contain abundant personalized information and indicate health status and disease deterioration. However, in current medical practice, clinicians working in the general wards are usually lack of plentiful means and tools to continuously monitor the physiological signals of the inpatients. To address this problem, we here presented a medical-grade wireless monitoring system based on wearable and artificial intelligence technology. The system consists of a multi-sensor wearable device, database servers and user interfaces. It can monitor physiological signals such as electrocardiography and respiration and transmit data wirelessly. We highly integrated the system with the existing hospital information system and explored a set of processes of physiological signal acquisition, storage, analysis, and combination with electronic health records. Multi-scale information extracted from physiological signals and related to the deterioration or abnormality of patients could be shown on the user interfaces, while a variety of reports could be provided daily based on time-series signal processing technology and machine learning to make more information accessible to clinicians. Apart from an initial attempt to implement the system in a realistic clinical environment, we also conducted a preliminary validation of the core processes in the workflow. The heart rate veracity validation of 22 patient volunteers showed that the system had a great consistency with ECG Holter, and bias for heart rate was 0.04 (95% confidence interval: −7.34 to 7.42) beats per minute. The Bland-Altman analysis showed that 98.52% of the points were located between Mean ± 1.96SD. This system has been deployed in the general wards of the Hyperbaric Oxygen Department and Respiratory Medicine Department and has collected more than 1000 cases from the clinic. The whole system will continue to be updated based on clinical feedback. It has been demonstrated that this system can provide reliable physiological monitoring for patients in general wards and has the potential to generate more personalized pathophysiological information related to disease diagnosis and treatment from the continuously monitored physiological data.

In laser-induced selective metallization (LISM), conventional laser activators only work at a single laser wavelength. This study reported a new laser activator (MoO3) very suitable for both 355 nm UV and 1064 nm near-infrared (NIR) lasers for the first time. When applying MoO3 to polymers, the prepared Cu layer on laser-activated polymers showed a good conductivity (2.63 × 106 Ω–1·m–1) and excellent adhesion. Scanning electron microscopy, optical microscopy, and resistance analysis revealed the excellent LISM performance of the polymer/MoO3 composites, and the quality of the Cu layer prepared using the UV laser is much better than that using the NIR laser. The limit width of the copper wire prepared by the UV laser is as narrow as 30.1 μm. We also confirmed the mechanism of MoO3 initiating electroless copper plating after laser activation to be the autocatalytic mechanism, which is very different from the conventional reduction mechanism. The effect of laser activation was only to expose the MoO3 active species to the polymer surface. X-ray diffraction and tube experiments revealed that the activity of α·h-MoO3 was higher than that of α-MoO3. X-ray photoelectron spectroscopy indicated that a part of Mo6+ was reduced to Mo5+ during laser activations, leading to the increase of the oxygen vacancies in MoO3 and possibly further enhancing the activity of MoO3. Besides, the micro-rough structures caused by the laser on the polymer surface provided riveting points for successfully depositing the copper layer. The Ni–Cu, Ag–Cu, and Au–Ni–Cu layers were obtained via the continued deposit of other metals on the Cu layer. The resistances of these metal layers had much better stability than that of the neat Cu layer. Furthermore, the Au layer further enhanced the conductivity of the circuit. The proposed strategy is easy for large-scale industrial applications, which will greatly expand the application scenarios of the LISM field.