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Additive manufacturing (AM) of components using material extrusion (MEX) offers the potential for the integration of functions through the use of multi-material design, such as sensors, actuators, energy storage, and electrical connections. However, there is a significant gap in the availability of electrical composite properties, which is essential for informed design of electrical functional structures in the product development process. This study addresses this gap by systematically evaluating the resistivity (DC, direct current) of 14 commercially available filaments as unprocessed filament feedstock, extruded fibers, and fabricated MEX-structures. The analysis of the MEX-structures considers the influence of anisotropic electrical properties induced by the selective material deposition inherent to MEX. The results demonstrate that composites containing fillers with a high aspect ratio, such as carbon nanotubes (CNT) and graphene, significantly enhance conductivity and improve the reproducibility of MEX structures. Notably, the extrusion of filaments into MEX structures generally leads to an increase in resistivity; however, composites with CNT or graphene exhibit less reduction in conductivity and lower variability compared to those containing only carbon black (CB) or graphite. These findings underscore the importance of filler selection and composition in optimizing the electrical performance of MEX structures.

Cotton (Gossypium spp.), a crucial cash crop in the United States, requires the constant monitoring of growth parameters for informed decision-making. Recently, forecasting models have gained prominence for predicting canopy indicators, aiding in-season planning and management decisions to optimize cotton production. This study employed unmanned aerial system (UAS) technology to collect canopy cover (CC) data from a 40-hectare cotton field in Driscoll, Texas, in 2020 and 2021. Long short-term memory (LSTM) models, trained using 2020 data, were subsequently applied to forecast the CC values for 2021. These models were compared with real-time auto-regressive integrated moving average (ARIMA) models to assess their effectiveness in predicting the CC values up to 14 days in advance, starting from the 28th day after crop emergence. The results showed that multiple-input multi-step output LSTM models achieved higher accuracy in predicting the in-season CC values during the early growth stages (up to the 56th day), with an average testing RMSE of 3.86, significantly lower than other single-input LSTM models. Conversely, when sufficient testing data are available, single-input stacked-LSTM models demonstrated precision in CC predictions for later stages, achieving an average RMSE of 3.06. These findings highlight the potential of LSTM models for in-season CC forecasting, facilitating effective management strategies in cotton production.

The additive manufacturing of components using material extrusion (MEX) enables the integration of several materials into one component, including functional structures such as electrically conductive structures. This study investigated the influence of the selected additive MEX process on the resistivity of MEX structures. Specimens were produced from filaments and granules of an electrically conductive PLA and filled with carbon nanotubes and carbon black. Specimens were produced with a full-factorial variation of the input variables: extrusion temperature, deposition speed, and production process. The resistivity of the specimens was determined by four-wire measurement. Analysis of the obtained data showed that only the extrusion temperature had a significant influence on the resistivity of the MEX specimens. Furthermore, the impact of the nozzle diameter was evaluated by comparing the results of this study with those of a previous study, with an otherwise equal experimental setup. The nozzle diameter had a significant influence on resistivity and a larger nozzle diameter reduced the mean variance by an order of magnitude. The resistivity was lower for most process parameter sets. As the manufacturing process had no significant influence on the resistivity of MEX structures, it can be selected based on other criteria, e.g., the cost of feedstock.

Additive manufacturing (AM) of components using material extrusion (MEX) offers the potential for the integration of functions through the use of multi-material design, such as sensors, actuators, energy storage, and electrical connections. However, there is a significant gap in the availability of electrical composite properties, which is essential for informed design of electrical functional structures in the product development process. This study addresses this gap by systematically evaluating the resistivity (DC, direct current) of 14 commercially available filaments as unprocessed filament feedstock, extruded fibers, and fabricated MEX-structures. The analysis of the MEX-structures considers the influence of anisotropic electrical properties induced by the selective material deposition inherent to MEX. The results demonstrate that composites containing fillers with a high aspect ratio, such as carbon nanotubes (CNT) and graphene, significantly enhance conductivity and improve the reproducibility of MEX structures. Notably, the extrusion of filaments into MEX structures generally leads to an increase in resistivity; however, composites with CNT or graphene exhibit less reduction in conductivity and lower variability compared to those containing only carbon black (CB) or graphite. These findings underscore the importance of filler selection and composition in optimizing the electrical performance of MEX structures. [ABSTRACT FROM AUTHOR]

Additive manufacturing of components using the material extrusion (MEX) of thermoplastics enables the integration of multiple materials into a single part. This can include functional structures, such as electrically conductive ones. The resulting functional structure properties depend on the process parameters along the entire manufacturing chain. The aim of this investigation is to determine the influence of process parameters in filament production and additive manufacturing on resistivity. Filament is produced from a commercially available composite of polylactide (PLA) with carbon nanotubes (CNT) and carbon black (CB), while the temperature profile and screw speed were varied. MEX specimens were produced using a full-factorial variation in extrusion temperature, layer height and deposition speed from the most and least conductive in-house-produced filament and the commercially available filament from the same composite. The results show that the temperature profile during filament production influences the resistivity. The commercially available filament has a lower conductivity than the in-house-produced filament, even though the starting feedstock is the same. The process parameters during filament production are the main factors influencing the resistivity of an additively manufactured structure. The MEX process parameters have a minimal influence on the resistivity of the used PLA/CNT/CB composite.

The additive manufacturing of components using material extrusion (MEX) enables the integration of several materials into one component, including functional structures such as electrically conductive structures. This study investigated the influence of the selected additive MEX process on the resistivity of MEX structures. Specimens were produced from filaments and granules of an electrically conductive PLA and filled with carbon nanotubes and carbon black. Specimens were produced with a full-factorial variation of the input variables: extrusion temperature, deposition speed, and production process. The resistivity of the specimens was determined by four-wire measurement. Analysis of the obtained data showed that only the extrusion temperature had a significant influence on the resistivity of the MEX specimens. Furthermore, the impact of the nozzle diameter was evaluated by comparing the results of this study with those of a previous study, with an otherwise equal experimental setup. The nozzle diameter had a significant influence on resistivity and a larger nozzle diameter reduced the mean variance by an order of magnitude. The resistivity was lower for most process parameter sets. As the manufacturing process had no significant influence on the resistivity of MEX structures, it can be selected based on other criteria, e.g., the cost of feedstock. [ABSTRACT FROM AUTHOR]

Abstract The genus Nitrospira is considered to be the most widespread and abundant group of nitrite-oxidizing bacteria in many natural and man-made ecosystems. However, the ecophysiological versatility within this phylogenetic group remains highly understudied, mainly due to the lack of pure cultures and genomic data. To further expand our understanding of this biotechnologically important genus, we analyzed the high quality draft genome of “Nitrospira lenta” strain BS10, a sublineage II Nitrospira that was isolated from a municipal wastewater treatment plant in Hamburg, Germany. The genome of “N. lenta” has a size of 3,756,190 bp and contains 3968 genomic objects, of which 3907 are predicted protein-coding sequences. Thorough genome annotation allowed the reconstruction of the “N. lenta” core metabolism for energy conservation and carbon fixation. Comparative analyses indicated that most metabolic features are shared with N. moscoviensis and “N. defluvii”, despite their ecological niche differentiation and phylogenetic distance. In conclusion, the genome of “N. lenta” provides important insights into the genomic diversity of the genus Nitrospira and provides a foundation for future comparative genomic studies that will generate a better understanding of the nitrification process.

Downsizing well-established materials to the nanoscale is a key route to novel functionalities, in particular if different functionalities are merged in hybrid nanomaterials. Hybrid carbon-based hierarchical nanostructures are particularly promising for electrochemical energy storage since they combine benefits of nanosize effects, enhanced electrical conductivity and integrity of bulk materials. We show that endohedral multiwalled carbon nanotubes (CNT) encapsulating high-capacity (here: conversion and alloying) electrode materials have a high potential for use in anode materials for lithium-ion batteries (LIB). There are two essential characteristics of filled CNT relevant for application in electrochemical energy storage: (1) rigid hollow cavities of the CNT provide upper limits for nanoparticles in their inner cavities which are both separated from the fillings of other CNT and protected against degradation. In particular, the CNT shells resist strong volume changes of encapsulates in response to electrochemical cycling, which in conventional conversion and alloying materials hinders application in energy storage devices. (2) Carbon mantles ensure electrical contact to the active material as they are unaffected by potential cracks of the encapsulate and form a stable conductive network in the electrode compound. Our studies confirm that encapsulates are electrochemically active and can achieve full theoretical reversible capacity. The results imply that encapsulating nanostructures inside CNT can provide a route to new high-performance nanocomposite anode materials for LIB.