Processing Cost Research Articles

The New Identity of Alginate Composite with Bentonite as a Sustainable Catalytic Natural-Based Material: Methylene Blue Decolorization in Continuous Flow-Packed-Bed Reactor

Abstrac The new identity of alginate composites with bentonite as sustainable catalytic natural-based material for methylene blue (MB) decolorization in a continuous flow-packed-bed reactor was investigated. The small-scale materials were produced by drop-wise coupled freeze drying with natural organic raw ingredients, namely bentonite and alginate. The decolonization experiments were conducted by manipulating the bed depth, flow rate, and temperature systematically. The evaluation revealed that a substantial 66 mm diameter and a flow rate of 0.7 mL/min were required to attain a high removal effectiveness (99%) of continuous MB-colored dyes at a temperature of 45 °C. Alginate-based composites were very appropriate because of their facile manufacture, cost-effectiveness, biocompatibility, renewability, easy separability, absence of secondary pollutants, and ecological benignity. Estimation of the cost and environmental impact of raw material supply and processing were evaluated by using embodied energy and embodied CO2 criteria. The implementation of scale adjustments and material supply was anticipated to result in a reduction of overall expenses. Regarding the environmental impact of the material, the embodied energy values for our alginate-clay production process confirmed that freeze-drying had the highest proportion, similar to cost analysis. In summary, products based on alginate showed potential for effective decolorization in the dye industry. Further research was required to thoroughly assess the technical and commercial viability of these materials, including their unique material properties and appropriate manufacturing methods.

Operating Point Optimization of Agricultural Tractor–Implement Combinations as Constraint Optimization Problem

Increasing the process efficiency of agricultural tasks is a key measure to decrease overall costs and CO2 emissions. However, optimizing tractor–implement combinations is challenging due to the variety of processes and implements and the complexity of the powertrains in modern tractors. In addition, overall process efficiency is an ambiguous optimization objective in agricultural processes as it relates resource consumption to harvest yields, which are only known at the end of a harvest season. The presented approach defines process constraints, ensuring optimization does not negatively affect harvest yield. These constraints allow for the formulation of explicit objective functions that are observable during the operation. The method establishes a mathematical foundation for the optimization of agricultural processes. The mathematical principles of the theoretical framework and the techniques used to define control constraints are explored, whereby the applicability to alternative objectives like optimizing the overall process cost is highlighted. To demonstrate the practical utility of the proposed approach, an optimization cycle is applied to a real-world scenario: adapting the working speed during the tillage process using a cultivator to maximize energy efficiency. The approach simplifies the optimization problem by formulation as a constraint optimization problem, allowing for improving the operating point of tractor–implement combinations with respect to observable process objective functions. The results underline the importance of advanced control strategies in agricultural machinery, advancing precision agriculture and promoting sustainable farming practices.

Comparing sewage sludge vs. digested sludge for starting-up thermophilic two-stage anaerobic digesters: Operational and economic insights.

Despite advances in anaerobic digestion (AD), full-scale implementation faces significant challenges, particularly during the start-up phase, where inoculum selection is crucial. This study examines the impact of inoculum choice on the operational and economic performance of thermophilic digesters during the start-up phase. Methanogenic reactors R3 and R4 were inoculated with digested sludge (DiS) and diluted sewage sludge (DSS), respectively, and fed with hydrolyzed source-sorted organic fraction of municipal solid waste (SS-OFMSW) and thickened sewage sludge, which were processed in R1 and R2, serving as acidogenic reactors. A two-stage AD configuration was employed to mitigate inhibitory effects associated with the undigested inoculum (DSS). This approach enabled the establishment of methanogenic activity in R4 when the AD system is initiated with DSS. However, R3 outperformed R4, achieving 49 % of the feedstock's theoretical methane potential compared to 15 % in R4. Methane production and volatile solids (VS) processing costs in R4 were 18 and 3 times higher than in R3, respectively. R3's superior performance was attributed to DiS's diverse bacterial community, with over 66 % of genera involved in hydrolysis, volatile fatty acid production, and syntrophic methane production. In contrast, DSS was dominated by Trichococcus and Lactococcus (75.4 %), primarily involved in butyrate oxidation and lactate production. This study provides valuable insights into effective inoculum selection for the start-up of full-scale digesters.

SAP HCM time management and payroll integration: streamlining payroll processing

The integration of SAP HCM Time Management and Payroll systems has revolutionized workforce management by streamlining operations and enhancing organizational efficiency. This comprehensive examination explores how these integrated systems transform payroll processing through automated calculations, quality assurance mechanisms, and specialized case handling. The implementation benefits span across various sectors, particularly in manufacturing and healthcare, where complex scheduling and compliance requirements demand sophisticated solutions. Modern organizations leveraging cloud-based technologies and artificial intelligence have experienced significant improvements in processing accuracy, regulatory compliance, and cost efficiency. The integration framework demonstrates remarkable capabilities in handling diverse payroll scenarios, from basic time tracking to complex shift differentials and leave management. The emergence of predictive analytics and machine learning further enhances these systems' ability to detect anomalies, prevent fraud, and optimize workforce planning, setting new standards for payroll automation and workforce management.

How long is too long? Production-internal and communicative constraints in the coding of conditionality in Spanish

Abstract This study analyzes to what extent communicative accounts, in contrast to production ease ones, can predict the variable coding length of conditional constructions in Spanish. If coding length is determined by communicative efficiency, we anticipate more accessible antecedents to be more parsimoniously encoded and less accessible ones to be more explicitly encoded. Insofar as coding length is determined by production-internal processes, longer coding is expected when speakers experience production difficulties. A Bayesian mixed-effects multinomial regression of 977 conditional constructions indicates that coding length is chiefly regulated by the speaker’s communicative goals in that highly accessible antecedents are realized with shorter forms, and more inaccessible antecedents involve longer coding. We propose a speaker-hearer efficiency cline of conditional constructions that ranges from maximally explicit coding, which is less efficient for the speaker but easier to parse for the hearer, to maximally inferential coding, which is more efficient for the speaker but increases the inferential cognitive load of the hearer. Moreover, we find support for the view that pragmatically indirect conditionals are frequently redundantly coded. Because indirect speech act resolution is cognitively heavy for the listener, speakers seem to counterbalance the higher processing costs exerted by them through longer coding.

Enhancing Data Collection Time Intervals and Modeling the Structural Behavior of Bridges in Response to Temperature Variations

The impact of temperature on bridges represents one of the main long-term challenges of structural health monitoring (SHM). Temperature is an environmental variable that changes both throughout the day and between different seasons, and its variations can induce thermal loads on bridges, potentially resulting in considerable displacements and deformations. Therefore, it is essential to obtain current data on the impact of daily and seasonal temperature variations on bridge displacements. Unfortunately, the maintenance costs associated with using precise estimates of thermal loads in a bridge design are quite high. The introduction of more accessible structural monitoring services is imperative to increase the number of observed structures. Viable solutions to make SHM more efficient include minimizing the costs of equipment, sensors, data loggers, data transmission systems, or monitoring data processing software. This research aims to improve the time intervals for collecting data on external temperature variations measured on a bridge structure through a sensor-based detection system and the integration of results into a regression analysis model. The paper aims to determine the appropriate interval for capturing and transmitting the structural response influenced by temperature variations over a year and to develop a behavioral mathematical model for the concrete structural components of a monitored bridge. The structural behavior was modeled using the statistical software TableCurve 2D, v.5.01. The results indicate that extending the data collection periods from 15 min to 4 h, in a static regime, maintains the accuracy of the regression model; instead, the effects of this integration are a significant reduction in the costs of data collection, transmission, and processing. The practical implications of this study consist of improving the monitoring of the structural behavior of bridges and the prediction under thermal stress, aiding in the design of more resilient structures, and enabling the implementation of efficient maintenance strategies.

Mechanical and Thermal Properties of 3D-Printed Continuous Bamboo Fiber-Reinforced PE Composites

Continuous fibers with outstanding mechanical performance due to the continuous enhancement effect, show wide application in aerospace, automobile, and construction. There has been great success in developing continuous synthetic fiber-reinforced composites, such as carbon fibers or glass fibers; however, most of which are nonrenewable, have a high processing cost, and energy consumption. Bio-sourced materials with high reinforced effects are attractive alternatives to achieve a low-carbon footprint. In this study, continuous bamboo fiber-reinforced polyethylene (CBF/PE) composites were prepared via a facile two-step method featuring alkali treatment followed by 3D printing. Alkali treatment as a key processing step increases surface area and surface wetting, which promote the formation of mechanical riveting among bamboo fibers and matrix. The obtained treated CBF (T-CBF) also shows improved mechanical properties, which enables a superior reinforcement effect. 3D printing, as a fast and local heating method, could melt the outer layer PE tube and impregnate molten plastics into fibers under pressure and heating. The resulting T-CBF/PE composite fibers can achieve a tensile strength of up to 15.6 MPa, while the matrix PE itself has a tensile strength of around 7.7 MPa. Additionally, the fracture morphology of printed bulks from composite fibers shows the alkali-treated fibers–PE interface is denser and could transfer more load. The printed bulks using T-CBF/PE shows increased tensile strength and Young’s modulus, with 77%- and 1.76-times improvement compared to pure PE. Finally, the effect of printing paraments on mechanical properties were analyzed. Therefore, this research presents a potential avenue for fabricating continuous natural fiber-reinforced composites.

Machine Learning Based Finger Print Analysis for Gender Detection: A Review

Gender detection using fingerprint biometrics has emerged as a promising area of research due to its non-intrusive nature and potential applications in biometric identification systems. The procedure can involve multiple steps are the size of finger print and their ridge pattern, minutiae point, machine learning and image processing and accuracy and limitations. This review explores the effectiveness of machine learning techniques for gender classification based on fingerprint patterns, emphasizing the role of advanced classification algorithms and feature extraction methods. Machine learning is crucial for gender detection since it classifies fingerprint patterns and biometric information using models like Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). To identify traits unique to a gender, such as ridge density and minutiae points, these algorithms are trained using labelled datasets. Compared to manual procedures, these models are more effective at handling high-dimensional data and identifying subtle gender-related patterns. Although hybrid models like CNN-DNN and AlexNet further increase classification precision, Convolutional Neural Networks (CNNs) are especially effective due to their automatic feature extraction capabilities. Despite their effectiveness, factors like as picture resolution, demographic balance, and dataset heterogeneity might affect performance, highlighting the need for carefully selected datasets and improved model designs. A structured comparative analysis of multiple studies reveals the impact of various datasets, feature types, and model architectures on classification accuracy and reliability. The findings suggest that deep learning models often outperform traditional classifiers, while dimensionality reduction and hybrid approaches can further enhance performance. However, challenges such as dataset imbalances, limited diversity, and susceptibility to low-quality fingerprint data remain prominent barriers to achieving consistent results. This review also outlines key limitations observed across the studies and provides recommendations for future research, including the need for more diverse datasets and optimized classification frameworks. This study aims to improve fingerprint feature extraction for gender detection, reduce processing costs, fix dataset imbalances, and increase classification accuracy. By stating the objective, the scope and objectives of each investigation are made clear. The generalizability of machine learning models is significantly impacted by the amount, variety, and quality of the dataset. The analysis aims to support the development of more accurate, inclusive, and scalable fingerprint-based gender detection systems.

Simulation-Based Studies on FAGeI3-Based Lead (Pb2+)-Free Perovskite Solar Cells

In the recent reports, it is clear that lead-free perovskite materials with low band gaps are desirable candidates for photovoltaic cells. In this regard, it was observed that germanium (Ge) is a less toxic lead-free metal that is significant for the preparation of Ge-based perovskite materials. Ge-based perovskite materials, for example, methyl ammonium germanium iodide (MAGeI3), cesium germanium iodide (CsGeI3), and/or formamidinium germanium iodide (FAGeI3) may be the suitable absorber materials and alternatives towards the fabrication of lead-free photovoltaic cells. In the past few years, few attempts were made to develop FAGeI3-based perovskite solar cells, but their photovoltaic performance is still under limitations. This is indicating that some significant and effective strategies should be designed and developed for the construction of Ge-based perovskite solar cells. It is believed that optimization of layer thickness, device structure, and selection of a suitable electron transport layer (ETL) may improve the photovoltaic performance of FAGeI3-based perovskite solar cells. Solar cell capacitance simulation, i.e., SCAPS is one of the promising software programs that can provide significant theoretical findings for the development of FAGeI3-based perovskite solar cells. The simulation studies via SCAPS may benefit researchers to save their energy and high cost for the optimization process in the laboratories. In this research article, SCAPS was adopted as a simulation tool for the theoretical investigations of FAGeI3-based perovskite solar cells. The simulation studies exhibited the excellent efficiency of 15.62% via SCAPS. This study proposed the optimized device structure of FTO/TiO2/FAGeI3/PTAA/Au with enhanced photovoltaic performance.

Green Extraction of Carotenoids from Pumpkin By-Products Using Natural Hydrophobic Deep Eutectic Solvents: Preliminary Insights

Natural hydrophobic deep eutectic solvents (NaHDESs), composed of natural components like menthol, fatty acids, and organic acids, are sustainable alternatives to conventional solvents for extracting carotenoids from agro-industrial by-products. This study assessed the performance of nine NaHDESs for extracting β-carotene from pumpkin peels, identifying DL-menthol/lactic acid (1:2) as the most effective solvent, achieving a yield of 0.823 ± 0.019 mg/mL of β-carotene, corresponding to 93.95% of the yield obtained using acetone. Optimization through Box–Behnken design (BBD) and response surface methodology (RSM) established ideal extraction conditions: a molar ratio of HBA:HBD at 1:4, a solvent-to-sample ratio of 26:1, and an extraction time of 30 min. These conditions maximized β-carotene recovery while minimizing energy consumption and process costs. Using NaHDESs facilitates the valorization of food waste, achieving extraction efficiencies of up to 25.05% of the theoretical carotenoid content in pumpkin peels. Their high performance and environmentally friendly profile underscore the potential of NaHDESs as sustainable alternatives to conventional solvents.

Leveraging two-dimensional pre-trained vision transformers for three-dimensional model generation via masked autoencoders

Although the Transformer architecture has established itself as the industry standard for jobs involving natural language processing, it still has few uses in computer vision. In vision, attention is used in conjunction with convolutional networks or to replace individual convolutional network elements while preserving the overall network design. Differences between the two domains, such as significant variations in the scale of visual things and the higher granularity of pixels in images compared to words in the text, make it difficult to transfer Transformer from language to vision. Masking autoencoding is a promising self-supervised learning approach that greatly advances computer vision and natural language processing. For robust 2D representations, pre-training with large image data has become standard practice. On the other hand, the low availability of 3D datasets significantly impedes learning high-quality 3D features because of the high data processing cost. We present a strong multi-scale MAE prior training architecture that uses a trained ViT and a 3D representation model from 2D images to let 3D point clouds learn on their own. We employ the adept 2D information to direct a 3D masking-based autoencoder, which uses an encoder-decoder architecture to rebuild the masked point tokens through self-supervised pre-training. To acquire the input point cloud’s multi-view visual characteristics, we first use pre-trained 2D models. Next, we present a two-dimensional masking method that preserves the visibility of semantically significant point tokens. Numerous tests demonstrate how effectively our method works with pre-trained models and how well it generalizes to a range of downstream tasks. In particular, our pre-trained model achieved 93.63% accuracy for linear SVM on ScanObjectNN and 91.31% accuracy on ModelNet40. Our approach demonstrates how a straightforward architecture solely based on conventional transformers may outperform specialized transformer models from supervised learning.

Towards Scalable Production of Sodium‐Ion Batteries: Solvent‐Free Layered‐Oxide Cathodes and Aqueous‐Processed Hard Carbon Anodes for Cost‐Effective Full‐Cell Manufacturing.

Achieving commercial viability for more sustainable sodium‐ion batteries (SIB) necessitates reducing the environmental impact of production, particularly originating from electrode drying and the use of toxic solvents like N‐methyl‐2‐pyrrolidone (NMP). This study presents the dry‐processing of commercial P2‐type Na0.75Ni0.25Fe0.25Mn0.50O2 (NFM) via the DRYtraec® process, aiming to lower the binder content of 1 wt.% polytetrafluoroethylene (PTFE) and eliminating the need for electrode drying and NMP recovery. Assessments of electrode morphology and active material crystallinity were conducted to gauge the effects of mechanical stress during processing. The resulting cathodes, loaded at a commercially relevant 2.3‐2.7 mAh cm‐2 loading, were successfully paired with aqueous‐processed hard carbon (HC) anodes, demonstrating stable performance in full‐cells. Comparative analysis with entirely wet‐processed electrodes revealed comparable capacity accessibility and comparable long‐term stability. This showed the competitiveness of dry‐processed cathodes. Finally, the integration of NMP‐free, dry‐processed cathodes and aqueous‐processed anodes was scaled to the commercially relevant prototype pouch‐cell. The cell demonstrates stable cycling for 400 cycles with an energy density of 102 Wh kg‐1 as well as reduced processing costs and environmental footprint.

Common and distinct ERP responses to violations of two different types of politeness maxims during sentence comprehension.

An ERP experiment was conducted to investigate the common and distinct neurocognitive mechanisms underlying the on-line processing of two types of politeness maxims (self-depreciation and other-elevation) and the individual differences during sentence reading. Electroencephalograms were recorded while participants read sentences containing pragmatically appropriate or inappropriate honorific or humble terms. When collapsing all participants' data, inappropriate humble and honorific terms elicited N400 and P600 effects, respectively, which could reflect semantic processing costs and rechecking processes, respectively. More importantly, communication abilities modulated N400 and late negativity effects for appropriateness for humble but not honorific terms. In contrast, perspective-taking and emphatic concern modulated N400 and late positivity effects, respectively, for honorific but not humble terms. Moreover, some commonness of the appropriateness effect modulation by individual variables was also observed. These results are discussed in terms of the commonness and individuality of neurocognitive mechanisms underlying the processing of different politeness maxims during sentence comprehension.

Estimating Policy Functions in Payment Systems Using Reinforcement Learning

This paper uses reinforcement learning (RL) to approximate the policy rules of banks participating in a high-value payment system (HVPS). The objective of the RL agents is to learn a policy function for the choice of amount of liquidity provided to the system at the beginning of the day and the rate at which to pay intraday payments. Individual choices have complex strategic effects precluding a closed form solution of the optimal policy, except in simple cases. We show that in a stylized two-agent setting, RL agents learn the optimal policy that minimizes the cost of processing their individual payments—without complete knowledge of the environment. We further demonstrate that in more complex settings, both agents learn to reduce the cost of processing their payments and effectively respond to liquidity-delay trade-off. Our results show the potential of RL to solve liquidity management problems in HVPS and provide new tools to assist policymakers in their mandates of ensuring safety and improving the efficiency of payment systems.

Single-drive 2-DOF wideband micromirror via mode interaction.

Micromirror technology is one of the current research hotspots. In this work, what we believe to be a novel electrostatic 2-DOF micromirror structure with double-biased torsional axes is proposed. By introducing internal resonance, synchronous motions of the two axes with a locked frequency ratio under a single driving force were achieved within a wide frequency range. The mechanical structure can thus be greatly simplified, as well as the operating frequency band is broadened. Also, two driving methods were proposed to realize the density spot acquisition with a 1:2 frequency ratio. The macroscopic experiment is further carried out to verify the validity of the theoretical model, which successfully realized a 1:2 internal resonance. The structural optimization design of internal resonance micromirrors is discussed, and a band expansion of at least 135.58% can be achieved in the simulation results. Compared with the traditional resonant micromirrors, the proposed one greatly increases the operating band at a very small sacrifice of vibration amplitude, and the resonant state can be guaranteed in complex environments. This novel micromirror provides a new chip solution for portable devices in complex environments and greatly simplifies the structure of dual-axis resonant micromirrors, reduces processing costs, and improves processing reliability.

Comparative evaluation of various DNA extraction methods and analysis of DNA degradation levels in commercially marketed Chestnut rose juices and beverages

BackgroundFood safety is a significant global study subject that is strongly intertwined with human life and well-being. The utilization of DNA-based methods for species identification is a valuable instrument in the field of food inspection and regulation. It is particularly significant for traceability purposes, as it enables the monitoring of a specific item at every level of the food chain regulation. However, obtaining amplifiable genomic DNA in this process is a significant obstacle in gene studies. To date, there is a lack of literature on DNA extraction from processed juice or beverages, and no data exist on simultaneous comparisons of various extraction processes. This study aimed to optimize and compare four DNA extraction methods for Chestnut rose juices and beverages. Furthermore, we also conducted a comparison and analysis of the extent of DNA degradation in Chestnut rose juice or beverage by utilizing the amplicon size.MethodsThe quantity and quality of the extracted DNA were assessed using NanoDrop One spectrophotometer, gel electrophoresis, and real-time polymerase chain reaction (real-time PCR or qPCR) assays. An assessment was conducted on the processing time, labor intensity, and cost associated with each approach. The degree of DNA degradation in Chestnut rose juice or beverage was also assessed using TaqMan real-time PCR methods.ResultsThe non-commercial modified CTAB-based approach yielded a high DNA concentration. However, spectrophotometric results and real-time PCR analysis showed poor DNA quality. The combination approach showed the greatest performance among the extraction methods, while being comparatively time-consuming and costly in contrast to the other methods. Additionally, the analytical findings of DNA degradation suggested that the integrity of sample DNA could be influenced by the intricacy of processing methods used by various manufacturers.ConclusionsTo achieve precise DNA quantification, selecting suitable extraction strategies for the given matrix is necessary. The combination approach was identified as the most effective DNA extraction technique and is suggested for extracting DNA from Chestnut rose juices and beverages. This comparative assessment can be particularly valuable for extracting and identifying processed Juices and Beverages in a diverse range of food compositions.

A safe highway driving surface using an open graded concrete

The use of open graded asphalt wearing courses for highway pavements is established technology but the opportunities to use a similar cementitious based surface type for concrete pavements has been limited by material technology and costs. Research work in Australia using new generation concrete admixtures and placing techniques has allowed the development of a stiff open graded concrete material which is expected to meet the same durability requirements as standard concrete used for roads. Open graded surface textures absorb vehicle noise emissions and minimise water film build up during rain events, thus resulting in quieter and safer driving conditions. In addition, experience has shown that high void contents at the surface assist in reducing water spray generation and glare reflection. This paper details the laboratory mix design test results and three field trials using hand placing techniques to define the limits of a wet on wet construction process. The concrete developed in the trials has high compressive strength with air voids in the range of 20 to 30%. The next stage in the development of the open graded concrete surfacing is in the use of mechanised placing techniques to make the process cost competitive to other asphalt alternatives and yet meet similar durability requirements to those currently expected in concrete road surfaces over a 40 year design period.

Advances in polyhydroxyalkanoate (PHA) production from renewable waste materials using halophilic microorganisms: A comprehensive review.

Polyhydroxyalkanoates (PHAs) are biodegradable and biocompatible polymers that can replace conventional plastics in different sectors. However, PHA commercialization is hampered due to their high production cost resulting from the use of high purity substrates, their low conversion into PHAs by using conventional microbial chassis and the high downstream processing cost. Taking these challenges into account, researchers are focusing on the use of waste by-products as alternative low-cost feedstocks for fast-growing and contamination-resistant halophilic microorganisms (Bacteria, Archaea…). This is of great importance since these extremophiles can use low-cost substrates, produce high PHA content of copolymers or different PHA monomer compositions. They can present high potential for reducing the costs of PHA fermentation and recovery processes, making their use in commercial applications easier. However, little is known about the potential of halophiles in advancing the PHA production from renewable waste materials at lab-scale and their successful implementation at industrial-scale. This review presents actual advances in PHA production by halophilic pure/engineered species (e.g. Haloferax mediterranei, Halomonas spp.) and mixed microbial consortia (MMC) using organic waste streams. The development of optimal PHA production process involves robust genetic engineering strategies, advanced fermentation processes using mixed microbial consortia versus pure/engineered strains as well as algal biomass as feedstocks.

Diatom-Based Photobiological Treatment of Reverse Osmosis Concentrate: Optimization of Light and Temperature and Biomass Analysis

As global water scarcity intensifies, the desalination of brackish groundwater and surface water plays a critical role in augmenting water supplies. However, managing reverse osmosis concentrate (ROC) from brackish water desalination remains challenging due to silica and calcium accumulation and precipitation, which cause membrane scaling and reduce freshwater recovery. This study employed the brackish diatom Gedaniella flavovirens Psetr3 in a photobiological treatment to remove dissolved silica and calcium, offering a natural, sustainable solution to improve freshwater recovery. Optimal treatment conditions were identified, with a light intensity of 200 µmol m−2 s−1 and incubation temperatures between 23 °C and 30 °C maximizing silica uptake (up to 46 ± 3 mg/L/day) while minimizing diatom mortality. This study reports, for the first time, the silica, organic, and calcite content in diatom biomass and their production rates during the photobiological treatment of ROC using G. flavovirens Psetr3. The photobiological treatment of one million gallons (3785 m3) per day of ROC would produce 174 kg of silica, 163 kg of organics, and 314 kg of calcite daily. These findings provide valuable insights into the potential for utilizing these bioresources to offset the costs of photobiological treatment and subsequent desalination processes.

Bridging Experimentation and Computation: OMSP for Advanced Acrylate Characterization and Digital Photoresin Design in Vat Photopolymerization.

Vat photopolymerization (VPP) is an additive manufacturing method that requires the design of photocurable resins to act as feedstock and binder for the printing of parts, both monolithic and composite. The design of a suitable photoresin is costly and time-consuming. The development of one formulation requires the consumption of kilograms of costly materials, weeks of printing and performance testing, as well as the need to have developers with the expertise and knowledge of the materials used, making the development process cost thousands. This paper presents a new characterization methodology for acrylates that allows for the computerization of the photoresin formulation development process, reducing the timescale to less than a week. Okoruwa Maximum Saturation Potential (OMSP) is a methodology that uses attenuated total reflection (ATR-FTIR) to study the functional group of acrylates, assigning numerical outputs to characterize monomers, oligomers and formulations, allowing for more precise distinguishment between materials. It utilizes the principles of Gaussian normal distribution for the storage, recall, and computerization of acrylate data and formulation design without the need to database numerous files of spectral data to an average coefficient of determination (R2) of 0.97. The same characterization method can be used to define the potential reactivity of acrylate formulations without knowing the formulation components, something not possible when using properties such as functionality. This allows for modifications to be made to unknown formulations without prior knowledge of their contents. Validation studies were performed to define the boundaries of the operation of OMSP and assess the methodology's reliability as a characterization tool. OMSP can confidently detect changes caused by the presence of various acrylates made to the photoresin system and distinguish between acrylates of the same viscosity and functionality. OMSP can compare digitally mixed formulations to physically mixed formulations and provides a high degree of accuracy (R2 of 0.9406 to 0.9964), highlighting the future potential for building foundations for artificial intelligence in VPP; the streamlining of photoresin formulation design; and transforming the way acrylates are characterized, selected, and used.