Background Student engagement can predict successful learning outcomes and academic development. The expansion of simulation-based medical and healthcare education creates challenges for educators, as they must help students engage in a simulation-based learning environment. This research provides a reference for facilitators of simulation teaching and student learning in medical and health-related majors by providing a deep understanding of student engagement in a simulation-based learning environment. Methods We conducted semi-structured interviews with ten medical and healthcare students to explore their learning types and characteristics in a simulation-based learning environment. Thematic analysis was used to analyse the data. Results The interviews were thematically analysed to identify three types of student engagement in the simulation-based learning environment: reflective engagement, performance engagement, and interactive engagement. The analysis also identified eight sub-themes: active, persistent, and focused thinking engagement; self-directed-learning thinking engagement with the purpose of problem solving; active “voice” in class; strong emotional experience and disclosure; demonstration of professional leadership; interaction with realistic learning situations; support from teammates; and collegial facilitator-student interaction. Conclusions The student interview and thematic analysis methods can be used to study the richness of student engagement in simulation-based learning environments. This study finds that student engagement in a simulation-based learning environment is different from that in a traditional environment, as it places greater emphasis on performance engagement, which combines both thinking and physical engagement, as well as on interactive engagement as generated through interpersonal interactions. Therefore, we suggest expanding the learning space centring around “inquiry”, as it can help strengthen reflective communication and dialogue. It also facilitates imagination, stimulates empathy, and builds an interprofessional learning community. In this way, medical and healthcare students can learn through the two-way transmission of information and cultivate and reshape interpersonal relationships to improve engagement in a simulation-based learning environment.

No abstract

No abstract

In this work, a carbon black (VXC-72R)/zirconia (ZrO2) nanocomposite-modified glassy carbon electrode (GCE) was designed, and a VXC-72R/ZrO2/GCE-based electrochemical sensor was successfully fabricated for the high-sensitivity detection of methyl parathion (MP). Electrochemical measurements showed that the VXC-72R/ZrO2/GCE-based electrochemical sensor could make full use of the respective advantages of the VXC-72R and ZrO2 nanoparticles to enhance the MP determination performance. The VXC-72R nanoparticles had high electrical conductivity and a large surface area, and the ZrO2 nanoparticles possessed a strong affinity to phosphorus groups, which could achieve good organophosphorus adsorption. On the basis of the synergistic effect generated from the interaction between the VXC-72R and ZrO2 nanoparticles, the VXC-72R/ZrO2/GCE-based electrochemical sensor could show excellent trace analysis determination performance. The low detection limit could reach up to 0.053 μM, and there was a linear concentration range of 1 μM to 100 μM. Such a high performance indicates that the VXC-72R/ZrO2/GCE-based electrochemical sensor has potential in numerous foreground applications.

A series of silicon-doped lithium manganese oxides were obtained via a sol-gel process. XRD characterization results indicate that the silicon-doped samples retain the spinel structure of LiMn2O4. Electrochemical tests show that introducing silicon ions into the spinel structure can have a great effect on reversible capacity and cycling stability. When cycled at 0.5 C, the optimal Si-doped LiMn2O4 can exhibit a pretty high initial capacity of 140.8 mAh g−1 with excellent retention of 91.1% after 100 cycles, which is higher than that of the LiMn2O4, LiMn1.975Si0.025O4, and LiMn1.925Si0.075O4 samples. Moreover, the optimal Si-doped LiMn2O4 can exhibit 88.3 mAh g−1 with satisfactory cycling performance at 10 C. These satisfactory results are mainly contributed by the more regular and increased MnO6 octahedra and even size distribution in the silicon-doped samples obtained by sol-gel technology.

Accurate prediction of the state of health (SOH) is essential to ensure the safety and reliability of battery operation. The thermal factor is an important indicator of SOH and many methods based on temperature are sensitive to measurement noise. To improve SOH estimation precision, a new health indicator (HI) directly extracted from the temperature curve is developed and an integrated multi-Gaussian process regression (MGPR) model is proposed. First, based on the trend analysis of the charging temperature curves with battery degradation, three features that can reflect thermal characteristics are extracted as the HI. Second, considering that the model generated by machine learning is influenced by the training dataset and the inherent inconsistencies in batteries, MGPR model is proposed to improve the model fitness. The training data is reformed and multiple GPR models are established. The multiple models are weighed by taking into account the prediction uncertainty to get the final SOH estimation result. Finally, two types of open-source data relative to different ambient temperatures and operating profiles are used to verify the performance. Experiment results show that the HI developed can characterize the battery degradation well and the MGPR model has high robustness and can obtain high-precision estimation results.

In this work, LiMn2−xErxO4 (x ≤ 0.05) samples were obtained by sol-gel processing with erbium nitrate as the erbium source. XRD measurements showed that the Er-doping had no substantial impact on the crystalline structure of the sample. The optimal LiMn1.97Er0.03O4 sample exhibited an intrinsic spinel structure and a narrow particle size distribution. The introduction of Er3+ ions reduced the content of Mn3+ ions, which seemed to efficiently suppress the Jahn–Teller distortion. Moreover, the decreased lattice parameters suggested that a more stable spinel structure was obtained, because the Er3+ ions in a ErO6 octahedra have stronger bonding energy (615 kJ/mol) than that of the Mn3+ ions in a MnO6 octahedra (402 kJ/mol). The present results suggest that the excellent cycling life of the optimal LiMn1.97Er0.03O4 sample is because of the inhibition of the Jahn-Teller distortion and the improvement of the structural stability. When cycled at 0.5 C, the optimal LiMn1.97Er0.03O4 sample exhibited a high initial capacity of 130.2 mAh g−1 with an excellent retention of 95.2% after 100 cycles. More significantly, this sample showed 83.1 mAh g−1 at 10 C, while the undoped sample showed a much lower capacity. Additionally, when cycled at 55 °C, a satisfactory retention of 91.4% could be achieved at 0.5 C after 100 cycles with a first reversible capacity of 130.1 mAh g−1.

No abstract