46 Search Results

This Laboratory Analytical Procedure (LAP) describes the quantitative determination of total organic carbon, inorganic carbon in whole suspended biological samples and in the cell-free supernatant, by combustion and CO₂ detection with a non-dispersive infrared (NDIR) detector. This allows for distinct reporting of soluble and insoluble organic and inorganic carbon. In addition, this procedure covers the quantitative determination of soluble nitrogen in the cell-free supernatant.

Here this work proposes and applies a sequential approach of objective methods to aid the decision-making process for the deployment of microalgae biorefineries. The strategy combines Multicriteria Decision Analysis (MCDA) and weight assignment methods to simultaneously consider technical, economic, and environmental criteria to (1) outrank the best bioproduct options from different biomass fractions present in microalgae biomass at different ratios (namely carbohydrates, lipids, and protein) and (2) define the most suitable biorefining pathways associated with specific pairings of microalgae strains and cultivation conditions. The first part of the assessment identified succinic acid, acrylic acid, and citric acid as the top-rankedmore » bioproducts from carbohydrates, polyurethane from lipids, and thermoplastic extrusion co-feed from protein. The second step of the analysis determined that, when production of a hydrocarbon fuel is desired, the compositional profile of a strain is paramount in defining the biorefining setup that should be pursued. In summary, microalgae lipids should be sent to the production of hydrocarbon fuels if the ratio between neutral lipids and fermentable carbohydrates is higher than roughly 1, with carbohydrates and protein being converted to the higher-value products noted above. Finally, this result was corroborated through process simulations, which indicated superior economic and environmental metrics when strains are paired with suitable conversion pathways identified through MCDA based on their compositional profiles. The outcomes of this work provide clear, objective, guidelines for establishing the best biorefining approach for a large suite of biochemical compositions as a screening method prior to employing detailed process simulations alongside rigorous techno-economic and life-cycle assessments.« less

During large scale algal biomass cultivation, it is difficult to reliably control relative composition to target levels. Rapid determination of chemical composition is feasible by using near infrared (NIR) spectral data. We sought to build and improve on reliable high-throughput screening prediction method based on partial least squares regression (PLSR) by the application of artificial neural networks (ANN) and associated optimization strategies. The algal biomass sample set was designed and created in an iterative process of culturing in physiologically diverse conditions at the GAI field site, followed by compositional analyses at NREL. The workflow allowed us to identify gaps inmore » compositional space for informing the subsequent cultivation and sampling efforts and generated a high quality set of 210 unique samples with chemical analysis results, spectral scanning data, and cultivation metadata. We observed a significant improvement in the performance of carbohydrate content predictions using an optimized ANN model compared to PLSR, with > 16% reduction in mean absolute percent error (MAPE) when tested on the same set of reserved data. The optimized ANN models for FAME and protein prediction performed exceptionally well with 5.99% and 5.09% MAPE, respectively. Application of these methods to detection and quantification of minor biomass constituents that are relevant to certain product streams has shown positive preliminary results, opening the possibility for extensions to the outputs of this powerful data type. All models are accompanied by prediction uncertainties and unsupervised spectral outlier detection to alert an operator to unreliable spectral data. These tools can be deployed for rapid determination of algal culture status, and cultivation and biomass quality improvement.« less

To create carbon efficient sources of bioenergy feedstocks and feedstuff for aquaculture and terrestrial livestock, it is critical to develop and commercialize the most efficient seaweed cultivation approach with a sustainable nutrient input supply. Here, we present data for a novel, onshore tropical macroalgae cultivation system, based on influent deep seawater as the nutrient and carbon sources. Two red algal species were selected, Agardhiella subulata and Halymenia hawaiiana, as the basis for growth optimization. Highest productivity in small-scale cultivation was demonstrated with A. subulata in the 10% deep seawater (64.7 µg N L^–1) treatment, growing at up to 26% specific growthmore » rate day–1 with highest yields observed at 247.5 g m^–2 day^–1 fresh weight. The highest yields for H. hawaiiana were measured with the addition of 10% deep seawater up to 8.8% specific growth rate day^–1 and yields at 63.3 g fresh weight m^–2 day^–1 equivalent. Biomass should be culled weekly or biweekly to avoid density limitations, which likely contributed to a decrease in SGR over time. With a measured 30–40% carbon content of the ash-free dry weight (20–30% of the dry weight) biomass, this translates to an almost 1:1 CO₂ capture to biomass ratio. The compositional fingerprint of the high carbohydrate content of both Agardhiella and Halymenia makes for an attractive feedstock for downstream biorefinery applications. By focusing on scaling and optimizing seaweed farming technologies for large-scale onshore farms, the opportunities for yield potential, adaptability to cultivation conditions, and meeting global sustainability goals through novel, carbon-negative biomass sources such as seaweed can be realized.« less

In our previous studies, as described in the corresponding Part I and II manuscripts in this Special issue, we selected 12 promising microalgae strains from 38 strains for high biomass productivity based on their salinity preferences, temperature profiles, and biomass growth under simulated outdoor conditions. The 12 strains include 6 cold season strains (Micractium reisseri NREL14-F2, Monoraphidium minutum 26B-AM, Monoraphidium sp. MONOR1, Chlorella vulgaris LRB-AZ-1201, Tetraselmis striata LANL1001, Phaeodactylum tricornutum UTEX646) and 6 warm seasons strains (Scenedesmus obliquus DOE0152.z, Scenedesmus obliquus UTEX393, Chlorella sorokiniana DOE1116, Picochlorum renovo NREL39-A8, Picochlorum celeri TG2-WT-CSM/EMRE, Porphyridium cruentum CCMP7765). Because the preceding screening efforts weremore » completed in indoor systems, this study focuses on quantitatively assessing these strains in outdoor raceway ponds to further eliminate those perform poorly under outdoor conditions. The trials presented in this study, categorized as either cold or warm season experiments based on water temperature, were carried out between February and June in 2018, 2019 and 2020. For each season, a benchmark strain, M. minutum 26B-AM for the cold season and S. obliquus UTEX393 for the warm season, was grown side-by-side with the new strains to provide a baseline for comparing strains cultivated under different weather conditions. The results showed that the cold season benchmark, M. minutum 26B-AM, remained the most productive cold season strain, but two new strains, P. triconutum UTEX646 and T. striata LANL1001, demonstrated robust growth in the presence of weather changes and biological contaminants. For the warm season trials, P. celeri TG2-WT-CSM/EMRE and P. cruentum CCMP675 were 39% and 11% more productive than the benchmark with substantially improved culture stability. Biomass compositional analyses of the strains showed that the carbohydrate, lipid (measured as fatty acid methyl esters) and protein account for 5.2-20.6%, 7.2-11.9%, and 21.7-53%, of the ash-free dry matter, respectively.« less

Critically needed improvements in biomass and biofuel intermediate productivity can be made by addressing fundamental inefficiencies in algal carbon conversion efficiency (CCE) to biofuel intermediates. Algae photosynthesis is, at best, able to convert 5-7% of incident light energy to biomass, while conversion to fuel intermediates falls 15-25% short of its maximum potential due to inefficiencies along the pathways. Recent progress in the Rewiring Algal Carbon Energetics (RACER) project consortium focused on a means to address the above inefficiencies in a pathway from algal biomass to a trifecta of fuel intermediates, ethanol, 2,3-butane diol, lipids and green biocrude. This project engineeredmore » a production-relevant algal species Desmodesmus armatus (SE 00107), to demonstrate biomass productivity improvements, with a doubling of the fuel intermediate yields. The new algae biorefinery paradigm embodied in RACER opens opportunities for algae engineering beyond efforts typically targeted solely at lipid content or improved light harvesting efficiency. Parallel approaches showed improved CCE through elimination of wasted energy during photosynthesis and increased carbon flux to transitory carbohydrate storage in the cells. Outdoor operation and nutrient management strategies with improvements in pretreatment, fermentation and extraction in a Combined Algal Processing approach showed a 40% reduction in MFSP, with a combined biofuel productivity of > 3700 gal/acre.« less

Rationale Elucidating intra‐organismal biochemical and lipid organization in photosynthetic biological cell factories of filamentous cyanobacteria, such as Arthrospira platensis ( Spirulina ), is important for tracking physiological response mechanisms during growth. Little is known about the filaments' biochemical organization and cellular structure and no label‐free imaging techniques exist that provide molecular mapping. Methods We applied ultrahigh‐resolution mass spectrometry (MS) with matrix‐assisted laser desorption ionization (MALDI) imaging to immobilized Spirulina filaments to investigate the localization of lipids across distinct physiological regions. We optimized matrix selection and deposition methods with the goal of facilitating high spatial, and intra‐filament, resolution using untargeted multivariatemore » statistical spectral deconvolution across MS pixels. Results Our results demonstrate an improved two‐step matrix application with an optimized procedure for intra‐organismal lipid profiling to improve analyte sensitivity and achieve higher spatial resolution. We evaluate several conventional matrices, namely 2,5‐dihydroxybenzoic acid (DHB), superDHB (sDHB), 1,5‐diaminonaphthalene (DAN), and a 50:50 mix of DHB and sDHB, and compare delineation and pixel‐based elucidation of intra‐filament lipidomics. We identified a total of 1626 features that could be putatively assigned a lipid‐like formula based on database query and 46 unique features, with associated lipid assignments that were significantly distinct in their intra‐filament location. Conclusions MALDI imaging MS with untargeted statistical spectral deconvolution was used to visualize intra‐filament lipidomics organization in Spirulina filaments. Improvements in matrix deposition, including sequential sublimation and pneumatic spraying, increased signal abundance at high spatial resolution and allowed for identification of distinct lipid composition regions. This work outlines a methodology that may be used for micro‐ecological untargeted molecular phenotyping.« less

Addressing critical improvements in biomass productivity and associated biochemical composition is a priority for the economic and sustainable commercial development of biofuels and bioproducts from algae. Capitalizing on pathways that integrate engineering approaches with fundamental biochemistry of photosynthetic organisms will lead to a better understanding of the complex nexus of algae growth rates, productivity and composition. This project focuses on identifying the critical factors for economic development of fuel and bioproduct technologies. Algal compositional characteristics form the foundation of robust economic and business models. This project supports that foundation by developing and validating accurate compositional methods and disseminating them tomore » the greater community. Simultaneously, we build a deep understanding of the dynamic biochemical composition and carbon allocation for biomass value and conversion yields. A co-product portfolio developed under this project demonstrated a 30% increase in intrinsic value. Additionally, an integrated pipeline of molecular diversity mapping for product discovery with quantitative demonstration across species was deployed over the BETO algae program. The advances made here are highly relevant to BETO's multi-year program targets of reducing costs and integrating dynamic biomass composition with conversion processes to provide options for bioproducts, all leveraging the molecular diversity of algae.« less

The Development of Integrated Screening, Cultivar Optimization, and Verification Research (DISCOVR) collaborative consortium operated pre-pilot scale outdoor ponds to deliver much-needed multi-year, long-term and consistent, algae cultivation data relevant to understanding the current state of technology in terms of expected seasonal algae biomass productivity. Over the course of four years from 2018 to 2021, twelve identical 4.2 m² mini-ponds were run in triplicate sets to test strains and operational strategies demonstrated in small-, indoor photobioreactors, in pursuit of increasing overall algae areal productivity and projected farm yield. Fourteen different cultivars derived from a strain screening pipeline were tested. Through deliberatemore » seasonal crop rotation and improvements in operational strategies, annual biomass productivity increased from 11.6 to 17.6 g m^-2 day^-1, a > 50% increase over the 2018 baseline. Both brackish and marine strains were included and four out of the fourteen strains consistently yielded high productivity across multiple years; brackish strains Monoraphidium minutum (26BAM) and Scenedesmus obliquus (UTEX393), and marine strains Tetraselmis striata (LANL1001) and Picochlorum celeri (TG2). These freely available datasets, which represent nearly complete annual daily coverage of cultivation metrics including weather, pond temperature and pH, nutrients, and productivity, are unique in the public domain and seek to fill agronomic and operational knowledge gaps to help in the eventual commercialization of algal biofuels and bioproducts.« less

Assessing the seasonal biomass productivity and compositional shift dynamics under simulated outdoor culture conditions of the top 21 algae strains selected during Tier I flask screening is an important step in the further prioritization of strains with regard to outdoor pond cultivation. These top 21 strains were subjected to Tier II testing in the PNNL Laboratory Environmental Algae Pond Simulator (LEAPS) photobioreactors, simulating light and temperature conditions of 20 cm deep outdoor ponds during the Arizona winter and summer season. All strains were grown in two consecutive nutrient-replete batch culture experiments at their particular optimal medium salinity to quantify theirmore » respective seasonal linear-phase areal biomass productivities. To determine biomass compositional shifts in response to nutrient-depletion, the LEAPS cultures were allowed to enter a 9-day nutrient depletion phase at the end of the second batch run. The following strains were evaluated in winter-season climate-simulated cultures and are listed in the order from highest (7.9 g m-2 day-1) to lowest (2.3 g m-2 day-1) areal N-replete biomass productivity: Monoraphidium minutum 26B-AM, Tetraselmis striata LANL1001, Chlorella vulgaris LRB AZ-1201, Micractinium reisseri NREL14-F2, Monoraphidium sp. MONOR1, Chlorella vulgaris NREL4-C12, Scenedesmus obliquus UTEX393, Scenedesmus acutus LRB-AP-0401, Nannochloropsis oceanica CCAP849/10, and Stichococcus minutus CCALA727. The following strains were evaluated in summer-season climate-simulated cultures and are listed in the order from highest (31.8 g m-2 day-1) to lowest (14.2 g m-2 day-1) areal N-replete biomass productivity: Picochlorum renovo NREL39-A8, Scenedesmus obliquus UTEX393, Porphyridium cruentum CCMP675, Picochlorum celeri TG2-WT-CSM/EMRE, Chlorella sorokiniana DOE1116, Stichococcus minor CCMP819, Picochlorum oklahomensis CCMP2329, Chlorella sorokiniana DOE1412 (UTEXB3016), Scenedesmus rubescens NREL46B-D3, Picochlorum soloecismus DOE101, Tetraselmis striata LANL1001, Scenedesmus obliquus DOE 0152.z, and Agmenellum quadruplicatum UTEX2268. All LEAPS cultures experienced a significant reduction in areal biomass productivity in response to nutrient-depletion, from 7 to 16% in the winter season simulation and from 1 to 60% in the summer season simulation. For 10 of the strains tested, the carbohydrate content more than doubled upon nutrient depletion, and for 9 strains, the lipid content increased by over 50% of the initial content.« less