Advanced battery modeling for interfacial phenomena and optimal charging

Lithium-ion batteries are one of the most promising energy storage systems for portable devices, transportation, and renewable grids. To meet the increasing requirements of these applications, higher energy density and areal capacity, long cycle life, fast charging rate and enhanced safety for lithium-ion battery (LIBs) are urgently needed. To solve these challenges, the relevant physics at different length scale need to be understood. However, experimental study is time consuming and limited in small scale’s study. Modeling techniques provide us powerful tools to get a deep understanding of the relevant physics and find optimal solutions. This work focuses on studying the mechanism in advanced battery engineering techniques and developing a new charging algorithm by model-based optimization. The research topics are divided into six topics and each topic is reported as a form of journal publication. Paper Ⅰ provides a new aspect of how ALD coating improves the lithium-ion diffusion at electrode particles. Paper Ⅱ explains the mechanisms by which 3D electrodes enhance battery performance and reveals guidelines for optimized 3D electrode designs by a 3D electrochemical-mechanical battery model. Paper Ⅲ investigates the electrolyte concentration impact on SEI layer growth and Li plating, especially under high charge rates. Paper Ⅳ proposes an optimized charging protocol for fast charging for reducing the charging time with minimal degradation. Paper Ⅴ reports a comprehensive degradation model for degradation estimation and life predication of energy storage system (ESS). Paper Ⅵ is a study of temperature-dependent state of charge (SOC) estimation for battery pack -Abstract, p. i

El cultismo léxico en la obra poética de Rubén Darío

El presente estudio, cuyo título es El cultismo léxico en la obra poética de Rubén Darío, tiene como objetivo principal contribuir al estudio histórico del cultismo y valorar la capacidad innovadora de Rubén Darío (1867-1916) en la elaboración de un lenguaje poético propio. En las dos últimas décadas se han publicado trabajos muy completos en los que, sin embargo, no se contempla el léxico culto de Rubén Darío, maestro de escritores modernistas y, en general, de destacados nombres de la literatura hispánica de finales del siglo XIX y principios del siglo XX. Era necesario completar el capítulo del cultismo con un conocimiento más profundo de la función de este importantísimo elemento constitutivo en la poesía rubeniana. La primera dificultad que ha habido que superar es la delimitación del término cultismo respecto del significado de latinismo o arcaísmo. Así mismo, ha habido quedar una respuesta bien fundamentada sobre la adscripción del cultismo dentro de la categoría de los préstamos. Se han establecido unos criterios de identificación de los cultismos a la luz de las principales contribuciones sobre el tema con el fin de crear un corpus que nos permitiera cumplir los objetivos que se han propuesto..

NASICON-STRUCTURED SOLID LI-IONIC CONDUCTOR FOR NEXT WAVE OF ENERGY STORAGE DEVICES

Ph.DDOCTOR OF PHILOSOPH

The Future of ChatGPT-enabled Labor Market: A Preliminary Study

As a phenomenal large language model, ChatGPT has achieved unparalleled success in various real-world tasks and increasingly plays an important role in our daily lives and work. However, extensive concerns are also raised about the potential ethical issues, especially about whether ChatGPT-like artificial general intelligence (AGI) will replace human jobs. To this end, in this paper, we introduce a preliminary data-driven study on the future of ChatGPT-enabled labor market from the view of Human-AI Symbiosis instead of Human-AI Confrontation. To be specific, we first conduct an in-depth analysis of large-scale job posting data in BOSS Zhipin, the largest online recruitment platform in China. The results indicate that about 28% of occupations in the current labor market require ChatGPT-related skills. Furthermore, based on a large-scale occupation-centered knowledge graph, we develop a semantic information enhanced collaborative filtering algorithm to predict the future occupation-skill relations in the labor market. As a result, we find that additional 45% occupations in the future will require ChatGPT-related skills. In particular, industries related to technology, products, and operations are expected to have higher proficiency requirements for ChatGPT-related skills, while the manufacturing, services, education, and health science related industries will have lower requirements for ChatGPT-related skills

Zero-Shot Wireless Indoor Navigation through Physics-Informed Reinforcement Learning

The growing focus on indoor robot navigation utilizing wireless signals has stemmed from the capability of these signals to capture high-resolution angular and temporal measurements. Prior heuristic-based methods, based on radio frequency propagation, are intuitive and generalizable across simple scenarios, yet fail to navigate in complex environments. On the other hand, end-to-end (e2e) deep reinforcement learning (RL), powered by advanced computing machinery, can explore the entire state space, delivering surprising performance when facing complex wireless environments. However, the price to pay is the astronomical amount of training samples, and the resulting policy, without fine-tuning (zero-shot), is unable to navigate efficiently in new scenarios unseen in the training phase. To equip the navigation agent with sample-efficient learning and {zero-shot} generalization, this work proposes a novel physics-informed RL (PIRL) where a distance-to-target-based cost (standard in e2e) is augmented with physics-informed reward shaping. The key intuition is that wireless environments vary, but physics laws persist. After learning to utilize the physics information, the agent can transfer this knowledge across different tasks and navigate in an unknown environment without fine-tuning. The proposed PIRL is evaluated using a wireless digital twin (WDT) built upon simulations of a large class of indoor environments from the AI Habitat dataset augmented with electromagnetic (EM) radiation simulation for wireless signals. It is shown that the PIRL significantly outperforms both e2e RL and heuristic-based solutions in terms of generalization and performance. Source code is available at \url{https://github.com/Panshark/PIRL-WIN}.Comment: 16 pages, 13 figures, 4 table

Soil Storage Conditions Alter the Effects of Tire Wear Particles on Microbial Activities in Laboratory Tests

In this study, we focused on the fact that soil storage conditions in the laboratory have never been considered as a key factor potentially leading to high variation when measuring effects of microplastics on soil microbial activity. We stored field-collected soils under four different conditions [room-temperature storage, low-temperature storage (LS), air drying (AD), and heat drying] prior to the experiment. Each soil was treated with tire wear particles (TWPs), and soil microbial activities and water aggregate stability were investigated after soil incubation. As a result, microbial activities, including soil respiration and three enzyme activities (β-glucosidase, N-acetyl-β-glucosaminidase, and phosphatase), were shown to depend on soil storage conditions. Soil respiration rates increased with the addition of TWPs, and the differences from the control group (no TWPs added) were more pronounced in the AD TWP treatment than in soils stored under other conditions. In contrast, phosphatase activity followed an opposing trend after the addition of TWPs. The AD soil had higher phosphatase activity after the addition of TWPs, while the LS soil had a lower level than the control group. We suggest that microplastic effects in laboratory experiments can strongly depend on soil storage conditions

Risk prediction models for postoperative delirium in elderly patients with hip fracture: a systematic review

ObjectivesTo systematically evaluate the risk prediction models for postoperative delirium in older adult hip fracture patients.MethodsRisk prediction models for postoperative delirium in older adult hip fracture patients were collected from the Cochrane Library, PubMed, Web of Science, and Ovid via the internet, covering studies from the establishment of the databases to March 15, 2023. Two researchers independently screened the literature, extracted data, and used Stata 13.0 for meta-analysis of predictive factors and the Prediction Model Risk of Bias Assessment Tool (PROBAST) to evaluate the risk prediction models for postoperative delirium in older adult hip fracture patients, evaluated the predictive performance.ResultsThis analysis included eight studies. Six studies used internal validation to assess the predictive models, while one combined both internal and external validation. The Area Under Curve (AUC) for the models ranged from 0.67 to 0.79. The most common predictors were preoperative dementia or dementia history (OR = 3.123, 95% CI 2.108–4.626, p < 0.001), American Society of Anesthesiologists (ASA) classification (OR = 2.343, 95% CI 1.146–4.789, p < 0.05), and age (OR = 1.615, 95% CI 1.387–1.880, p < 0.001). This meta-analysis shows that these were independent risk factors for postoperative delirium in older adult patients with hip fracture.ConclusionResearch on the risk prediction models for postoperative delirium in older adult hip fracture patients is still in the developmental stage. The predictive performance of some of the established models achieve expectation and the applicable risk of all models is low, but there are also problems such as high risk of bias and lack of external validation. Medical professionals should select existing models and validate and optimize them with large samples from multiple centers according to their actual situation. It is more recommended to carry out a large sample of prospective studies to build prediction models.Systematic review registrationThe protocol for this systematic review was published in the International Prospective Register of Systematic Reviews (PROSPERO) under the registered number CRD42022365258