IMPROVING STUDENTS' CREATIVE THINKING SKILLS WITH PROJECT BASED LEARNING

This study aims to determine the effect of project-based learning (PBL) model on the creative thinking skills of class XI science students at MAN 1 Tapanuli Tengah on cell material. This research belongs to the type of quantitative descriptive research with data collection techniques are tests and non-tests that are carried out to measure students' creative thinking abilities. The sample in this study amounted to 2 classes, namely the experimental class and the control class. Data analysis used in the form of instrument calibration, data collection, data reduction, and data presentation. The results of Mann Whitney's hypothesis test on SPSS version 26 obtained a probability value (Asymp. Sig.) 0.116 < 0.05, Ho is accepted, meaning that there is no significant difference in each aspect of creative thinking ability between the control class and the experimental class. The results showed that the percentage of creative thinking ability of the experimental class was 60% and the control class was 50% with the criteria of moderate creative thinking ability. The conclusion in this study is that there is no significant effect of the application of project-based learning on students' creative thinking skills in cell material in class XI IPA MAN 1 Tapanuli Tengah.

Prediction of Solar Cell Materials via Unsupervised Literature Learning

Despite the significant advancement of the data-driven studies for physical science, the textual data that are numerous in the literature are not fully embraced by the physics and materials community. In this manuscript, we successfully employ the natural language processing (NLP) technique to unsupervisedly predict the existence of solar cell types including the dye-sensitized solar cells and the perovskite solar cells based on literatures published prior to their first discovery without human annotation. Enlightened by this, we further identify possible solar cell material candidates via NLP starting with a comprehensive training database of 3.2 million paper abstracts published before 2021. The NLP model effectively predicts the existing solar cell materials, while an uncommon solar cell material namely PtSe2 is suggested as an appropriate candidate for the future solar cells. Its optoelectronic properties are comprehensive investigated via first-principles calculations to reveal the decent stability and optoelectronic performance of the NLP-predicted candidate. This study demonstrates the viability of the textual data for the data-driven materials prediction and highlights the NLP method as a powerful tool to reliably predict the solar cell materials.

Development of Learning Tools Based on Mind Mapping Worksheet for Improving Stundents’ Creative Thinking Skills on Cell Material

Life skills-based education is an important tool to face today's global demands, one of which is creative thinking skills. This study aims to determine the feasibility of learning tools based on mind mapping worksheet to practice creative thinking skills for class XI MIPA on cell material, which are viewed in terms of (1) validity, (2) practicality and (3) effectiveness. The learning tools developed are syllabus, lesson plans, LKDP and tests of creative thinking skills. The mind mapping worksheet that was trained included indicators of creative thinking skills, fluency, flexibility, originality and elaboration, which were tested on 23 students of Class XI MIPA SMA Al-Islam Krian. This study used a 4D design with one group pretest-posttest design, the data obtained were analyzed descriptively. The results of the validity of the device get mode 4 with a very valid category. The results of the practicality of the device were carried out with mode 4 in the very good category. The effectiveness of the device is based on the average N-gain score ranging from 0.5 to 0.92. The result of students' mastery of the concept of the material is 86.3 in the complete category. Students' responses to the four LKPD mind mapping developed were 81% in the positive category.

An Assessment Framework for Solar Cell Material Based on a Modified Fuzzy DEMATEL Approach

We propose an evaluation system to choose appropriate materials for solar cells. A fuzzy DEMATEL information procedure was used for decision-making to gather information and analyze the casual relationship. These data acquired were partitioned into causal and impact bunches, empowering users to gather an improved understanding of the intelligent relationship among them, as well as making recommendations for changes to upgrade their general execution. The proposed approach can deliver a compelling fabric choice assessment with satisfactory criteria that fit the respondent’s discernment designs; particularly, these evaluation dimensions are interlaced. Recommendations are given to assist government authorities to plan a Taiwan solar cell industry approach and for industries to develop commerce techniques for improvement in the solar cell field.

Engineering Design of Battery Module for EVs: Comprehensive Framework Development Based on DFT, Topology Optimization, Machine Learning, Multidisciplinary Design Optimization and Digital Twins

Battery technology has been a hot spot for many researchers lately. Electrochemical researchers have been focusing on the synthesis and design of battery materials; researchers in the field of electronics have been studying the simulation and design of battery management system (BMS); whereas mechanical engineers have been dealing with structural safety and thermal management strategies for batteries. However, overcoming battery limitation in only one or two domains will not design an efficient battery pack as it requires an integrated framework. So far, there are few research studies that circumscribed all the multi-disciplinary aspects (cell material selection, cell-electrode design, cell clustering, state of health (SOH) estimation, thermal management, cell monitoring and recycling) simultaneously for battery packs in electric vehicles (EVs). This paper presents a holistic engineering design and simulation strategy for a future advanced battery pack and its parts by assimilating paradigmatic solutions for cell material selection, component design, cell clustering, thermal management, battery monitoring and recycling aspects of the battery and its components. The developed framework has been proposed based on DFT based cell material selection, topology design based cell-electrode design, machine learning (ML) based SOH estimation along with multi-disciplinary design optimization based liquid cooling system. The proposed framework also highlights the optimal configuration of cells using ML algorithms and multi-objective optimization of cell-assembly parameters. The role of digital twins for real-time and faster acquisition of data has been highlighted for the advanced and futuristic battery pack designs. Furthermore, preliminary investigation of robot assisted disassembly and recycling of battery packs has been summarized. Each proposed methodology has been discussed in detail, along with the advantages and limitations. Critical research orientations are also discussed in the end.