Electrode processing plays an important role in advancing lithium-ion battery technologies and has a significant impact on cell energy density, manufacturing cost, and throughput. Compared to the extensive research on materials development, however, there has been much less effort in this area. In this Review, we outline each step in the electrode processing of lithium-ion batteries from materials to cell assembly, summarize the recent progress in individual steps, deconvolute the interplays between those steps, discuss the underlying constraints, and share some prospective technologies. This Review aims to provide an overview of the whole process in lithium-ion battery fabrication from powder to cell formation and bridge the gap between academic development and industrial manufacturing.

From Materials to Cell: State-of-the-Art and Prospective Technologies for Lithium-Ion Battery Electrode Processing.

Drones in agriculture: A review and bibliometric analysis

The machine learning (ML) field has deeply impacted the manufacturing industry in the context of the Industry 4.0 paradigm. The industry 4.0 paradigm encourages the usage of smart sensors, devices,...

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2021.1956675

Machine learning in manufacturing and industry 4.0 applications

A literature survey of the robotic technologies during the COVID-19 pandemic.

In resource poor settings where automated hematology analyzers are not available, the Cyanmethemoglobin method is often used. This method though cheaper, takes more time. In blood donations, the semi-quantitative gravimetric copper sulfate method which is very easy and inexpensive may be used but does not provide an acceptable degree of accuracy. The HemoCue® hemoglobin photometer has been used for these purposes. This study was conducted to generate data to support or refute its use as a point-of-care device for hemoglobin estimation in mobile blood donations and critical care areas in health facilities. EDTA blood was collected from study participants drawn from five groups: pre-school children, school children, pregnant women, non-pregnant women and men. Blood collected was immediately processed to estimate the hemoglobin concentration using three different methods (HemoCue®, Sysmex KX21N and Cyanmethemoglobin). Agreement between the test methods was assessed by the method of Bland and Altman. The Intraclass correlation coefficient (ICC) was used to determine the within subject variability of measured hemoglobin. Of 398 subjects, 42% were males with the overall mean age being 19.4 years. The overall mean hemoglobin as estimated by each method was 10.4 g/dl for HemoCue, 10.3 g/dl for Sysmex KX21N and 10.3 g/dl for Cyanmethemoglobin. Pairwise analysis revealed that the hemoglobin determined by the HemoCue method was higher than that measured by the KX21N and Cyanmethemoglobin. Comparing the hemoglobin determined by the HemoCue to Cyanmethemoglobin, the concordance correlation coefficient was 0.995 (95% CI: 0.994-0.996, p < 0.001). The Bland and Altman's limit of agreement was -0.389 - 0.644 g/dl with the mean difference being 0.127 (95% CI: 0.102-0.153) and a non-significant difference in variability between the two measurements (p = 0.843). After adjusting to assess the effect of other possible confounders such as sex, age and category of person, there was no significant difference in the hemoglobin determined by the HemoCue compared to Cyanmethemoglobin (coef = -0.127, 95% CI: -0.379 - 0.634). Hemoglobin determined by the HemoCue method is comparable to that determined by the other methods. The HemoCue photometer is therefore recommended for use as on-the-spot device for determining hemoglobin in resource poor setting.

/pdf/hemoglobin-estimation-by-the-hemocue-portable-hemoglobin-3f7vcr2zo1.pdf

Hemoglobin estimation by the HemoCue® portable hemoglobin photometer in a resource poor setting

Manufacturing is undergoing a paradigmatic shift as it assimilates and is transformed by machine learning and other cognitive technologies. A new paradigm usually necessitates a new framework to co...

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2021.1930234

The interpretive model of manufacturing: a theoretical framework and research agenda for machine learning in manufacturing

Consumer demand for Electric Vehicles (EVs) is increasing due to improving performance and affordability. However, EV manufacturers are struggling to meet this rise in demand. A key bottleneck is supply from a nascent EV battery supply chain that is new and developing. In this paper, we propose robotic work cell design for fast and reliable assembly of EV battery modules, at scale, to reduce this demand-supply gap.

Enabling the Electric Future of Mobility: Robotic Automation for Electric Vehicle Battery Assembly

The COVID-19 pandemic unraveled the weak points in the global supply chain for goods. Specifically, people all over the world, including those in the most advanced nations have had to go without medical supplies and personal protective equipment. Scarcity of essentials increases anxiety and uncertainty exacerbating unproductive behaviors like hoarding and price gouging. Left to market forces, such unfair practices are likely to aggravate hardships and increase the loss of lives. Thus, there is a critical need to ensure safe distribution of food and essential supplies to all citizens to sustain them through challenging times. To this end, we propose a simple, affordable and contact-less robotic system for preparing and dispensing food and survival-kits at community scale. The system has provisions to prevent hoarding and price gouging. Design, simulation, and, validation of the system has been completed to ensure readiness for real world implementation. This project is part of an open-source program and detailed designs are available upon request to entities interested in using it to serve their communities.

/pdf/drive-through-robotics-robotic-automation-for-last-mile-2n8irn2u4c.pdf

Drive Through Robotics: Robotic Automation for Last Mile Distribution of Food and Essentials During Pandemics

The turbid specimen as an analytical medium: hemoglobin determination as a model

On-line clarification for the measurement of serum glucose in hyperlipidemic specimens.

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