3D Printing-Enabled Design and Manufacturing Strategies for Batteries: A Review

Lithium-ion batteries (LIBs) have significantly impacted the daily lives, finding broad applications in various industries such as consumer electronics, electric vehicles, medical devices, aerospace, and power tools. However, they still face issues (i.e., safety due to dendrite propagation, manufacturing cost, random porosities, and basic & planar geometries) that hinder their widespread applications as the demand for LIBs rapidly increases in all sectors due to their high energy and power density values compared to other batteries. Additive manufacturing (AM) is a promising technique for creating precise and programmable structures in energy storage devices. This review first summarizes light, filament, powder, and jetting-based 3D printing methods with the status on current trends and limitations for each AM technology. The paper also delves into 3D printing-enabled electrodes (both anodes and cathodes) and solid-state electrolytes for LIBs, emphasizing the current state-of-the-art materials, manufacturing methods, and properties/performance. Additionally, the current challenges in the AM for electrochemical energy storage (EES) applications, including limited materials, low processing precision, codesign/comanufacturing concepts for complete battery printing, machine learning (ML)/artificial intelligence (AI) for processing optimization and data analysis, environmental risks, and the potential of 4D printing in advanced battery applications, are also presented.

Biomimetic 3D Prototyping of Hierarchically Porous Multilayered Membranes for Enhanced Oil-Water Filtration

This study introduces a biomimetic approach to 3D printing multilayered hierarchical porous membranes (MHMs) using Direct Ink Writing (DIW) technology. Fabricated through a fast layer-by-layer printing process with varying concentrations of pore-forming agents, the produced MHMs mimic the hierarchical pore structure and filtration capabilities of natural soil systems. As a result, the 3D-printed MHMs achieved an impressive oil rejection rate of 99.02% and demonstrated exceptional reusability, maintaining a flux recovery ratio of 99.48% even after hours of continuous filtration. Moreover, the 3D-printed MHMs exhibit superior hierarchical porous architecture and mechanical integrity compared to traditional flat sheet single-layered membranes. This study presents a significant advancement for scalable 3D printing of customized multilayer membranes with tailored porosity and high-performance filtration properties. The simplicity, versatility, and cost-effectiveness of the presented manufacturing method offer a pathway for advanced design and on-demand membrane production.

Cervicovaginal Metabolome and Tumor Characteristics for Endometrial Cancer Detection and Risk Stratification

PURPOSE

Endometrial cancer is highly prevalent and lacking noninvasive diagnostic techniques. Diagnosis depends on histological investigation of biopsy samples. Serum biomarkers for endometrial cancer have lacked sensitivity and specificity. The objective of this study was to investigate the cervicovaginal environment to improve the understanding of metabolic reprogramming related to endometrial cancer and identify potential biomarker candidates for noninvasive diagnostic and prognostic tests.

EXPERIMENTAL DESIGN

Cervicovaginal lavages were collected from 192 participants with endometrial cancer (n = 66) and non-malignant conditions (n = 108), and global untargeted metabolomics was performed. Using the metabolite data (n = 920), we completed a multivariate biomarker discovery analysis.

RESULTS

We analyzed grade 1/2 endometrioid carcinoma (n = 53) and other endometrial cancer subtypes (n = 13) to identify shared and unique metabolic signatures between the subtypes. When compared to non-malignant conditions, downregulation of proline (P < 0.0001), tryptophan (P < 0.0001), and glutamate (P < 0.0001) was found among both endometrial cancer groups, relating to key hallmarks of cancer including immune suppression and redox balance. Upregulation (q < 0.05) of sphingolipids, fatty acids, and glycerophospholipids was observed in endometrial cancer in a type-specific manner. Furthermore, cervicovaginal metabolites related to tumor characteristics, including tumor size and myometrial invasion.

CONCLUSIONS

Our findings provide insights into understanding the endometrial cancer metabolic landscape and improvement in diagnosis. The metabolic dysregulation described in this article linked specific metabolites and pathophysiological mechanisms including cellular proliferation, energy supply, and invasion of neighboring tissues. Furthermore, cervicovaginal metabolite levels related to tumor characteristics, which are used for risk stratification. Overall, development of noninvasive diagnostics can improve both the acceptability and accessibility of diagnosis.

Identification of volatile metabolites produced from levodopa metabolism by different bacteria strains of the gut microbiome

Interspecies pathways in the gut microbiome have been shown to metabolize levodopa, the primary treatment for Parkinson's disease, and reduce its bioavailability. While the enzymatic reactions have been identified, the ability to establish the resulting macromolecules as biomarkers of microbial metabolism remains technically challenging. In this study, we leveraged an untargeted mass spectrometry-based approach to investigate volatile organic compounds (VOCs) produced during levodopa metabolism by Enterococcus faecalis, Clostridium sporogenes, and Eggerthella lenta. We cultured these organisms with and without their respective bioactive metabolites and detected levodopa-induced shifts in VOC profiles. We then utilized bioinformatics to identify significant differences in 2,6-dimethylpyrazine, 4,6-dimethylpyrimidine, and 4,5-dimethylpyrimidine associated with its biotransformation. Supplementing cultures with inhibitors of levodopa-metabolizing enzymes revealed specific modulation of levodopa-associated diazines, verifying their relationship to its metabolism. Furthermore, functional group analysis depicts strain-specific VOC profiles that reflect interspecies differences in metabolic activity that can be leveraged to assess microbiome functionality in individual patients. Collectively, this work identifies previously uncharacterized metabolites of microbe-mediated levodopa metabolism to determine potential indicators of this activity and further elucidate the metabolic capabilities of different gut bacteria.

Large Clusters of Invasive emm49 Group A Streptococcus Identified Within Arizona Health Care Facilities Through Statewide Genomic Surveillance System, 2019-2021

A statewide genomic surveillance system for invasive Group A Streptococcus was implemented in Arizona in June 2019, resulting in 1046 isolates being submitted for genomic analysis to characterize emm types and identify transmission clusters. Eleven of the 32 identified distinct emm types comprised >80% of samples, with 29.7% of all isolates being typed as emm49 (and its genetic derivative emm151). Phylogenetic analysis initially identified an emm49 genomic cluster of 4 isolates that rapidly expanded over subsequent months (June 2019 to February 2020). Public health investigations identified epidemiologic links with 3 different long-term care facilities, resulting in specific interventions. Unbiased genomic surveillance allowed for identification and response to clusters that would have otherwise remained undetected.

Understanding the exposure risk of aerosolized Coccidioides in a Valley fever endemic metropolis

Coccidioides is the fungal causative agent of Valley fever, a primarily pulmonary disease caused by inhalation of fungal arthroconidia, or spores. Although Coccidioides has been an established pathogen for 120 years and is responsible for hundreds of thousands of infections per year, little is known about when and where infectious Coccidioides arthroconidia are present within the ambient air in endemic regions. Long-term air sampling programs provide a means to investigate these characteristics across space and time. Here we present data from > 18 months of collections from 11 air sampling sites across the Phoenix, Arizona, metropolitan area. Overall, prevalence was highly variable across space and time with no obvious spatial or temporal correlations. Several high prevalence periods were identified at select sites, with no obvious spatial or temporal associations. Comparing these data with weather and environmental factor data, wind gusts and temperature were positively associated with Coccidioides detection, while soil moisture was negatively associated with Coccidioides detection. These results provide critical insights into the frequency and distribution of airborne arthroconidia and the associated risk of inhalation and potential disease that is present across space and time in a highly endemic locale.