Wet Chemical Analysis Research Articles

Weed control and weed biomass influenced first cutting forage accumulation and nutritive value of spring‐seeded alfalfa

AbstractWeeds can influence the economics of alfalfa (Medicago sativa L.) production by reducing forage yield and nutritive value or by contaminating hay. Field studies were conducted in Idaho in 2021 and 2022 to evaluate the effect of weed control treatments on alfalfa forage accumulation, weed biomass, and nutritive value. In addition, the relationship between the proportion of individual weed species biomass and alfalfa nutritive value was assessed. These studies included eight different herbicide and herbicide combination treatments, including the untreated check. Treatments were comprised of preemergence, early postemergence (after 80% alfalfa emergence), and postemergence (third trifoliate alfalfa) herbicide applications. Data collection included weed control efficacy, weed and alfalfa biomass, and alfalfa nutritive value. Additional samples were collected and combined in these alfalfa to weed biomass proportions (percentage by weight): 0/100, 20/80, 40/60, 60/40, 80/20, and 100/0, for wet chemistry analysis of forage nutritive value to evaluate the relationship between the proportion of individual weed species biomass and alfalfa nutritive value. The acetochlor‐only treatment provided less than 50% weed control, while the EPTC (S‐ethyl‐N,N‐dipropylthiocarbamate)‐only treatment provided 54%–81% weed control. The control provided by acetochlor and EPTC was less than that provided by treatments containing imazamox and imazamox plus bromoxynil. Weed biomass in forage (23%–55% of total biomass) due to poor or no weed control reduced crude protein, increased fiber concentrations, and reduced the relative feed value. The relationship between the proportion of individual weed species biomass and alfalfa nutritive value was linear for all weed species evaluated.

Development of Wet Chemical Analysis Technique for Tramp Elements (M = As, Pb, Sb, and Sn) in Silver-M Alloys

Wet chemical analysis techniques for four elements (M = As, Pb, Sb, and Sn) in Ag – M binary alloys were investigated with emphasis on the choice of solvent acid and the characteristic wavelength used in the ICP-AES (Inductively-Coupled Plasma Atomic Emission Spectrometer) analysis. The elements are representative tramp elements in ferrous scrap. The activity of these elements needs to be increased to remove them efficiently during the molten scrap refining process. The activity of these elements in the molten scrap (molten iron alloy) is usually measured by a chemical equilibration technique with molten Ag. Therefore, performing an accurate and reliable chemical analysis of these elements in the molten iron alloy and the molten Ag alloy is important. Preliminary tests using conventional acids (hydrochloric acid, nitric acid) resulted in unreliable results. In the present study, the proper choice of acids as solvents was investigated for each element M in the Ag-M alloys. Several synthesized Ag-M alloys of known compositions were analyzed using two ICP-AES systems independently, for cross-checking. As and Pb in Ag alloys could be successfully dissolved in the nitric acid-based solution. On the other hand, Sb and Sn in Ag alloys did not dissolve in the nitric acid-based solution completely, leaving some precipitates. It was found that the addition of hydrofluoric acid could resolve this problem. In addition to this, the effect of the mass of the Ag-M alloy and wavelength selection during ICP-AES analysis on the accuracy and the reproducibility were investigated. An optimized procedure for the wet chemical analysis of these elements in Ag-M alloys is reported.

Composition of renders and plasters of award-winning buildings in Lisbon (Portugal): A contribution to the knowledge of binders used in the 20th Century

ABSTRACT This paper reports on the study of renders and plasters from 20th-century award-winning buildings in Lisbon (Portugal) with the Valmor Prize for Architecture. The mortars have been investigated through XRD, optical and electronic microscopy (SEM-EDS), thermal analysis (TGA/DTA), wet chemical analyses and AAS. The results reveal that the use of air lime lasted until the 1940s. It also highlights the beginning of using of Portland cement in mortars in the 1930s, mixed with air-lime, and the abandonment of mortar formulations solely based on air lime. This study highlights the use of finishing lime-gypsum-based mortars until the 1940s and different types of Portland cement from the 1940s onwards. Portland cement was the main binder for the analysed stone-imitating mortars from 1940s to 1970s. Finally, salt contamination was occasionally found in mortars, which generally reveal a good state of conservation.

Predicting Nutritional Quality of Dual-Purpose Cowpea Using NIRS and the Impacts of Crop Management

Cowpea fodder has been one of the favored livestock forages for centuries in sub-Saharan Africa, particularly in Senegal. However, little research has been conducted on quantifying the nutritional quality of cowpea fodder because of the costly wet chemistry analysis. The main objective of this study was to develop predictive equations for a sustainable quantification of the nutritional quality of dual-purpose cowpea fodder using near infrared spectroscopy (NIRS) and to investigate the influence of cropping system, fertilizer, genotype, and their interaction on biomass yield and cowpea forage nutritional value. In this study, 120 samples from a dual-purpose cowpea variety trial were used to develop NIRS equations to estimate forage quality parameters including concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), calcium (Ca), phosphorus (P), potassium (K), and iron (Fe). Partial least squares (PLS) regression generated prediction equations using NIRS wavelength measurements, and reference wet chemistry analysis from calibration samples were developed. The PLS prediction equations for the different forage quality parameters had an R2 of calibration 0.94, 0.93, 0.88, 0.63, 0.69, 0.87, and 0.94 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. Using these prediction equations, correlation of the predicted values of the calibration subset and the prediction test subset resulted in significant positive relationships, with R2 of 0.83, 0.74, 0.70, 0.63, 0.59, 0.75, and 0.83 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. The corresponding RMSE of these relationships was 0.91, 2.68, 3.45, 0.23, 0.06, 0.11, and 100 for CP, ADF, NDF, Ca, P, K, and Fe, respectively. The range and mean concentrations of the calibration subset overlapped with that of the prediction subset for all parameters evaluated. Cross-validation procedures indicated good correlations between wet chemistry analysis and NIRS forage quality estimates. Results of the second experiment showed that the cropping system had no significant effect on cowpea forage yield and nutritive value. However, cowpea variety and fertilizer, both individually and their interaction, had a significant effect on fodder yield and cowpea forage quality. We conclude that the NIRS calibration equations developed can be used to accurately predict the cowpea forage quality parameters evaluated in this study.

Determining Composition of Bamboo using Thermogravimetric Analysis (TGA)-Chemometrics Technique

The chemical composition of biomass plays a pivotal role in determining the processing routes, operational costs, and product quality of bioprocesses. Current wet chemistry analysis methods are time consuming and energy intensive making them unideal for industrial scale applications where productivity is important. In this study, suitability of thermogravimetric analysis coupled with chemometrics (herein referred to as TGA-Chemometrics technique) was evaluated in determining the composition of cellulose, lignin, and hemicellulose in bamboo. Bambusa balcooa and Bambusa bambos species were used and thermal analysis data was collected under oxidative and inert conditions. Then, the data was used to develop predictive models (regression models) to predict the composition of bamboo. The model-predicted results were compared to the results obtained via the wet chemistry analysis to assess the efficacy of the developed predictive models. The results indicated that the predictive models developed using the data from both oxidative and non-oxidative thermal decomposition were accurate at predicting the amount of cellulose, lignin, and hemicellulose with R2 and RMSEp values of 0.82–0.95 and 0.6–1.79, respectively. Moreover, the developed chemometric models developed from the data obtained under inert conditions displayed a better prediction than those developed from the data obtained under oxidative conditions, indicated by their lower RMSEp values. The difference in prediction was not large, showing an average absolute error of 1.49%. In conclusion, this study shows that TGA coupled with chemometric is a potential technique to obtain composition of biomass, making it an alternative method to the wet chemistry analysis method.

Chemical composition and antioxidant/antibacterial depictions of Zahidi date palm (Phoenix dactylifera) kernel Oil

Date palm “Phoenix dactylifera” variety Zahidi is a unique well-known cultivar. Mature fruits are semi-dry, medium size, cylindrically shaped and golden brown. However, there are three stages to harvesting the dates soft, hard or semi-soft. In this research, the oil processed from the seed/kernel of the date palm Zahidi is chemically analysed for its composition, antioxidant capacity and its antibacterial properties were investigated. Date seeds were ground and extracted for the oil. Wet chemical analyses were implemented on the extracted oil via various analytical techniques such as high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC–MS). Oil fractions of ethanol, methanol and acetone were tested for antibacterial activity, which was evaluated employing the Kirby–Bauer disc-diffusion method. Antibacterial action was examined for four bacterial strains; two of them representing Gram-positive such as Bacillus subtilis, and Staphylococcus aureus, and the other two for Gram-negative such as Pseudomonas aeruginosa and Escherichia coli. The antibiotics tetracycline, chloramphenicol, streptomycin, gentamycin and vancomycin were used as positive standards. The Minimum Bactericidal Concentration (MBC) and Minimum Inhibitory Concentration (MIC) of the bacterial strains were tested and evaluated. The antioxidant activity was evaluated as free-radical scavenging capacity (RSC) towards 2,2 - diphenyl -1-picrylhydrazyl (DPPH) radicals for the methanol fraction (MF), remaining lipid fraction (LF) and non-fractioned oil, i.e., the total fraction (TF). The RSCs for the MF, LF and TF were 42.5, 9.1 and 68.3, respectively. Zahidi seed/kernel oil extract inhibited all of the test microorganisms, with MICs that ranged from 10 to 42 mg/mL and MBCs were found to be ranged between 21 and 167 mg/mL. These outcomes suggest that the extracts could be developed as a foundation of expected antimicrobial complexes to fight unwanted bacteria in foods. Overall, the results showed that the oil is suitable for food, cosmetics, personal care products and pharmaceuticals.

Proximal sensing provides clean, fast, and accurate quality control of organic and mineral fertilizers

Farms use large quantities of fertilizers from many sources, making quality control a challenging task, as the traditional wet-chemistry analyses are expensive, time consuming and not environmentally-friendly. As an alternative, this work proposes the use of portable X-ray fluorescence (pXRF) spectrometry and machine learning algorithms for rapid and low-cost estimation of macro and micronutrient contents in mineral and organic fertilizers. Four machine learning algorithms were tested. Whole (i.e., as delivered by the manufacturer) (CP) and ground (AQ) samples (429 in total) were analyzed to test the effect of fertilizer granulometry in prediction performance. Model validation indicated highly accurate predictions of macro (N: R2 = 0.92; P: 0.97; K: 0.99; Ca: 0.94, Mg: 0.98; S: 0.96) and micronutrients (B: 0.99; Cu: 0.99; Fe: 0.98; Mn: 0.91; Zn: 0.94) for both organic and mineral fertilizers. RPD values ranged from 2.31 to 9.23 for AQ samples, and Random Forest and Cubist Regression were the algorithms with the best performances. Even samples analyzed as they were received from the manufacturer (i.e., no grinding) provided accurate predictions, which accelerate the confirmation of nutrient contents contained in fertilizers. Results demonstrated the potential of pXRF data coupled with machine learning algorithms to assess nutrient composition in both mineral and organic fertilizers with high accuracy, allowing for clean, fast and accurate quality control. Sensor-driven quality assessment of fertilizers improves soil and plant health, crop management efficiency and food security with a reduced environmental footprint.

Investigation of the degree of cross-linking of polyethylene and thermosets using absolute optical spectroscopy and Raman microscopy

Abstract. In the research work presented here, an integrating sphere demonstrator which is suitable for the non-destructive determination of the degree of cross-linking or curing and has the potential for use as an at-line device for in-process quality assurance was assembled and explored. The measurement system allows the analysis of absorption and scattering coefficients of materials independently by means of absolute optical spectroscopy. The two optical parameters showed a good correlation with the degree of cross-linking of cross-linked polyethylene (PE-X) and the degree of curing of different thermosets and adhesives, each of which was determined using different reference methods (wet chemical analysis, differential scanning calorimetry (DSC), and dielectric analysis (DEA)). The results show that different PE-X materials can be distinguished well by their absorption and scattering in the visual (VIS) and near-infrared (NIR) wavelength range, respectively, and conclusions on their degree of cross-linking are possible. Also, the curing of resins can be monitored based on the absorption. In addition, Raman spectroscopy was used to achieve a better understanding of the material changes during the cross-linking of the materials. It also showed a good suitability for monitoring the curing processes in thermosets. In summary, the new method can be used to determine the crucial parameters of these industrial important material types and fulfils the great demand for fast, non-destructive testing, which can be carried out during the process or on the finished product.

Metabolic Modeling of Hermetia illucens Larvae Resource Allocation for High-Value Fatty Acid Production.

All plant and animal kingdom organisms use highly connected biochemical networks to facilitate sustaining, proliferation, and growth functions. While the biochemical network details are well known, the understanding of the intense regulation principles is still limited. We chose to investigate the Hermetia illucens fly at the larval stage because this stage is a crucial period for the successful accumulation and allocation of resources for the subsequent organism's developmental stages. We combined iterative wet lab experiments and innovative metabolic modeling design approaches to simulate and explain the H. illucens larval stage resource allocation processes and biotechnology potential. We performed time-based growth and high-value chemical compound accumulation wet lab chemical analysis experiments on larvae and the Gainesville diet composition. We built and validated the first H. illucens medium-size, stoichiometric metabolic model to predict the effects of diet-based alterations on fatty acid allocation potential. Using optimization methods such as flux balance and flux variability analysis on the novel insect metabolic model, we predicted that doubled essential amino acid consumption increased the growth rate by 32%, but pure glucose consumption had no positive impact on growth. In the case of doubled pure valine consumption, the model predicted a 2% higher growth rate. In this study, we describe a new framework for researching the impact of dietary alterations on the metabolism of multi-cellular organisms at different developmental stages for improved, sustainable, and directed high-value chemicals.

Effect of Cordierite Additions on Mechanical Properties of Hydroxyapatite Used in Medical Applications

In this study, a novel bioceramic material with improved mechanical properties was prepared to be consentient with the mechanical requirements of living bodies. Cordierite was synthesized via the sintering method at 1400 °C, based on a composition of 51.4% silica, 34.8% of alumina, and 13.8% magnesia. The composition of precursor materials was studied using wet chemical analysis. Three different compositions of the cordierite (30, 40, and 50 wt.%) were incorporated in hydroxyapatite and then sintered at 700, 900, and 1100 °C. To visualize the characteristics of all prepared materials, scanning electron microscopy (SEM) and X-ray diffraction (XRD) were analyzed. The crystallite size was determined from XRD patterns using the Scherrer equation. Microhardness was investigated using (a digital micro hardness tester), while the compressive strength was tested using a microcomputer-controlled electronic universal testing machine (WDW-5E). The modulus of elasticity was determined from the stress-strain curve. As a result, all samples which contain cordierite powder have considerable mechanical properties as compared to the control sample. The incorporation of cordierite caused a slight increase in grain size at all sintering temperatures. Among all ratios, the second mixture, which includes 40% cordierite showed the best mechanical properties at all sintering temperatures. The percentage of 40% cordierite led to increasing the compressive strength of HA by 126.27% at 700 °C, 61.52% at 900 °C, and 123.05% at 1100 °C. On the other hand, the effect of this cordierite level on hardness increased by 94.04% at 700 °C, 141.29% at 900 °C, and 128.44% at 1100 °C. The values of elasticity modulus ranged between 15-37 GPa, i.e. consistent with that of cortical bone.

Introducing Pharmaceutics to Middle School Students using with Hypothesis-Driven, At-Home Activities

Since the COVID-19 pandemic, there has been an increase in healthcare and related science majors at the undergraduate level. This presents an opportunity to introduce health science training in middle school science classes to familiarize students to various healthcare-related fields. The pharmaceutical science-based hands-on activities for middle school student presented here used over-the-counter vitamin supplements and inexpensive equipment for its easy replication at home or in an educational setting. The two main objectives of the activities were to signify the instructions of drug administration on labels of vitamin C effervescent tablets and to introduce effective drug delivery of iron supplement syrup. These activities followed Kolb’s framework of experiential learning theory through four key steps: (1) design a testable hypothesis, (2) identify experimental variables and controls, (3) incorporate wet chemistry and safety analysis into the process, and (4) data recording and presentation. This online and distance outreach model can be adapted in online K-8 summer or afterschool programs which can especially benefit students in rural regions or those with limited mobility. Furthermore, the pre- and post-activity sheets corroborated student’s ability to (a) combine their knowledge of kitchen chemistry and laboratory safety skills through active experimentation, and (b) reflect on the experimental results obtained for test variables and compare those to their original hypotheses.

Unbiased prediction errors for partial least squares regression models: Choosing a representative error estimator for process monitoring

Partial least squares (PLS) regression is widely used to predict chemical analytes from spectroscopic data, thus reducing the need for expensive and time-consuming wet chemical reference analysis in industrial process monitoring. However, predictions via PLS by definition carry sample-specific errors, and estimation of these errors is essential for correct interpretation of results. To increase trust in PLS regression-based predictions, reliable prediction error estimates must be reported. This can be achieved by determining realistic sample-specific prediction errors using an unbiased mean squared prediction error estimate. This work provides a guide for estimating sample-specific prediction errors, showing the importance of choosing an appropriate error estimator prior to deploying PLS models for industrial applications. We reviewed recent and established methods for estimating the sample-specific prediction error and test them through simulation studies. The methods were subsequently applied for estimating prediction errors in two real-life datasets from the food ingredients industry, where near-infrared spectroscopy was used to quantify i) urea in process water and ii) individual protein concentrations in ultrafiltration retentates from a protein fractionation process. Both the simulations and real data examples showed that the mean squared error of calibration is always a downward biased estimator. Although leave-one-out-cross-validation performed surprisingly well in the data analysed in this work, this paper demonstrated that the appropriate choice of error estimator requires the user to make an informed, data-centered decision.

112 Effects of storage-handling methods on nutrient analysis of mixed-grass pasture samples

Forage should be the basis of any equine diet, even for those with insulin dysregulation (ID). Fresh and preserved forage low in nonstructural carbohydrates (NSC) is recommended for ID animals, indicating the need for an accurate forage analysis. It remains to be fully understood how handling fresh forage samples pre-analysis impacts nutrient values. The objectives of this study were to compare 5 pasture sample storage-handling methods, analyzed after 24h and 1wk, on various nutrients including crude protein (CP), water soluble carbohydrates (WSC), ethanol soluble carbohydrates (ESC), starch and NSC. In October 2022, forage samples were manually harvested to ground height between 0800 and 0900 from a mixed-grass pasture after a rest period. Subsamples were mixed to create triplicate samples for analysis at both time periods (24h vs 1 wk) and the following 5 storage-handling methods: microwave-oven (70s then stored at −20°C; MO), room temperature (RT), 3°C, −20°C, and −80°C, totaling 30 samples. All forage samples were shipped on ice to a commercial laboratory for wet chemistry and NIR analysis (Equi-Analytical, Ithaca, NY). Data were analyzed using 2-way ANOVAs in JMP with fixed effects of storage method, time and storage by time interactions as well as storage-handling method by laboratory analysis method and method by storage interactions.Statistical significance was declared at P ≤ 0.05. All data reported are from wet chemistry analysis as there were no significant differences between the 2 analysis techniques. Storage by time interactions were statistically different for some nutrients measured from the mixed grass when stored at RT. Specifically, the WSC (9.2 ± 0.4% DM vs. 5.4 ± 0.9% DM; P = 0.009) and ESC (5.4 ± 0.4% DM vs. 3.6 ± 0.6% DM; P = 0.001) content decreased from 24h compared with 1wk. The remaining storage-handling methods of MO, 3°C, −20°C, and −80°C, however, showed no statistical differences across time for WSC, Starch or NSC indicating these methods were appropriate for storing forage for this time. A decrease in CP content from 24h compared with 1wk was, however, shown when stored at −80°C (12.7 ± 0.6% DM vs. 11.2 ± 0.3% DM; P = 0.02), yet no other statistical differences in CP were detected. These results demonstrate that samples kept at RT or −80°C for one week before nutrient analysis may not accurately represent the nutrient content of the forage. Storage-handling method and time did affect the nutrient content of fresh forage thus, samples should be either immediately dried, kept at 3°C, or frozen (−20°C) preceding analysis.

PRISMA and Sentinel-2 spectral response to the nutrient composition of grains

Micronutrient malnutrition is a global challenge affecting >2 billion people, in particular those with a crop-based diet and limited access to nutrient-rich food sources. Conventional methods for measuring the crop nutrients such as wet chemical analysis of grains are time-consuming and cost-prohibitive and, consequently, unsuitable for the consistent quantification of nutrients across space and time. In this study, we propose a new method that is using PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Sentinel-2 images to estimate the nutrient concentrations of crop grains before harvest.We collected grain samples for corn, rice, soybean, and wheat from a farm situated in Italy and measured their nutrient concentrations in the lab. These measurements together with the PRISMA and Sentinel-2 images acquired at the main phases of crop development (vegetative, reproductive, maturity) were used as input for two-band vegetation indices (TBVIs) and Partial Least Squares Regression (PLSR) to predict Calcium (Ca), Iron (Fe), Potassium (K), Magnesium (Mg), Nitrogen (N), Phosphorus (P), Sulphur (S) and Zinc (Zn). Models' performances were assessed using the coefficient of determination (R2) and Root Mean Square Error (RMSE). For PRISMA images, the best prediction results were obtained for P in soybean (R2 = 0.69), K in soybean (R2 = 0.66), Mg in soybean (R2 = 0.58), Fe in soybean (R2 = 0.57), K in wheat (R2 = 0.57), K in corn (R2 = 0.55), P in wheat (R2 = 0.51), S in rice (R2 = 0.58) using TBVIs. In contrast to PRISMA, PLSR outperformed TBVIs when Sentinel-2 images were used as input. For Sentinel-2, the best predictions were obtained for P in soybean (R2 = 0.73), K in wheat (R2 = 0.67), Mg in soybean (R2 = 0.62), Zn in wheat (R2 = 0.56), Fe in soybean (R2 = 0.52), P in wheat (R2 = 0.52). Our study showed that estimating the nutrient composition of crops using remote sensing images has the potential to change how we approach a cost-effective, timely, and spatially explicit representation of the crops' nutritional quality.

Variations in Total Protein and Amino Acids in the Sequenced Sorghum Mutant Library.

Sorghum (Sorghum bicolor) is the fifth most important cereal crop worldwide; however, its utilization in food products can be limited due to reduced nutritional quality related to amino acid composition and protein digestibility in cooked products. Low essential amino acid levels and digestibility are influenced by the composition of the sorghum seed storage proteins, kafirins. In this study, we report a core collection of 206 sorghum mutant lines with altered seed storage proteins. Wet lab chemistry analysis was conducted to evaluate the total protein content and 23 amino acids, including 19 protein-bound and 4 non-protein amino acids. We identified mutant lines with diverse compositions of essential and non-essential amino acids. The highest total protein content in these lines was almost double that of the wild-type (BTx623). The mutants identified in this study can be used as a genetic resource to improve the sorghum grain quality and determine the molecular mechanisms underlying the biosynthesis of storage protein and starch in sorghum seeds.

Boosting the Activity of the Oxygen Evolution Reaction through an Electrospun Nickel Manganese-Based Bimetallic Zeolite Imidazolate Framework Fibrous System in Alkaline Medium.

Developing cost-efficient and noble metal free electrocatalysts is vastly anticipated for the oxygen evolution reaction (OER). Therefore, in this study, to lift the thermodynamic and kinetic activity of the OER, we attempted to synthesize a bimetallic nickel and manganese-based zeolite imidazolate framework system in a fiber form. For this synthesis, a bottom-up approach has been followed through wet chemical analysis, and electrospinning was utilized for fiber formation. The resultant fiber has shown a lesser overpotential of 256 mV at a benchmarking current density of 10 mA cm-2 under 1 M KOH conditions. As expected, the attained Tafel slope and charge transfer resistance values are lesser. The observed results reveal that the synergism between the Ni and Mn nodes on the imidazolate framework successfully promotes the thermodynamic formation of *O and *OOH intermediates, which significantly helps to improve the faster OER kinetics at the electrode-electrolyte interface.

Transformations of Ferrihydrite-Extracellular Polymeric Substance Coprecipitates Driven by Dissolved Sulfide: Interrelated Effects of Carbon and Sulfur Loadings.

The association of poorly crystalline iron (hydr)oxides with organic matter (OM), such as extracellular polymeric substances (EPS), exerts a profound effect on Fe and C cycles in soils and sediments, and their behaviors under sulfate-reducing conditions involve complicated mineralogical transformations. However, how different loadings and types of EPS and water chemistry conditions affect the sulfidation still lacks quantitative and systematic investigation. We here synthesized a set of ferrihydrite-organic matter (Fh-OM) coprecipitates with various model compounds for plant and microbial exopolysaccharides (polygalacturonic acids, alginic acid, and xanthan gum) and bacteriogenic EPS (extracted from Bacillus subtilis). Combining wet chemical analysis, X-ray diffraction, and X-ray absorption spectroscopic techniques, we systematically studied the impacts of C and S loadings by tracing the temporal evolution of Fe mineralogy and speciation in aqueous and solid phases. Our results showed that the effect of added OM on sulfidation of Fh-OM coprecipitates is interrelated with the amount of loaded sulfide. Under low sulfide loadings (S(-II)/Fe < 0.5), transformation to goethite and lepidocrocite was the main pathway of ferrihydrite sulfidation, which occurs more strongly at pH 6 compared to that at pH 7.5, and it was promoted and inhibited at low and high C/Fe ratios, respectively. While under high sulfide loadings (S(-II)/Fe > 0.5), the formation of secondary Fe-S minerals such as mackinawite and pyrite dominated ferrihydrite sulfidation, and it was inhibited with increasing C/Fe ratios. Furthermore, all three synthetic EPS proxies unanimously inhibited mineral transformation, while the microbiogenic EPS has a more potent inhibitory effect than synthetic EPS proxies compared at equivalent C/Fe loadings. Collectively, our results suggest that the quantity and chemical characteristics of the associated OM have a strong and nonlinear influence on the extent and pathways of mineralogical transformations of Fh-OM sulfidation.

Individual animal variability in rumination, activity, and lying behavior during the periparturient period of dairy cattle

The aim of the current study was to determine individual animal variability in rumination, activity, and lying behavior during the periparturient period within the context of dairy cattle nutrition, social, and physical environment. Holstein animals (nulliparous = 77, parous = 219) from one sand-bedded, freestall dairy in northwest Wisconsin were enrolled −17 d in milk (DIM, d 0 = calving), when they were fitted with an automated monitoring device (Hi-Tag, SCR Engineers Ltd.). At −11 DIM, animals were fitted with HOBO Pendant G Data Loggers. The HOBO Pendant G Data Loggers were fitted 6 d later because they were set up to collect data for 22 d (d −11 to 11), to avoid constant handling of the animals that could alter their behavior. Prepartum, nulliparous and parous animals were housed separately. Postpartum (1 to 17 ± 3 DIM), primiparous and multiparous cows were commingled. Samples of the total mixed ration were submitted for wet chemistry analysis and determination of physically effective NDF (peNDF). Temperature and humidity data were collected using RH Temp probes (HOBO Pro Series) installed in each of the pens, and the percentages of 30-min intervals within a day with temperature-humidity index ≥68 (PctTHI68) were calculated. Stocking density (cows per stall) during the pre- and postpartum periods were calculated daily. Prepartum data from nulliparous and parous animals were analyzed separately, and postpartum data from primiparous and multiparous animals were analyzed together. Prepartum, nulliparous and parous animals explained 83.9 and 64.5% of the variability in rumination, 70.7 and 60.9% of the variability in activity, and 38.1 and 63.6% of the variability in lying time, respectively. Postpartum, animal explained 49.7, 56.8, and 35.6% of the variability in rumination, activity, and lying time, respectively. Although stocking density, PctTHI68, peNDF, crude protein, and ether extract were associated with the variability in rumination, activity, and lying time, they explained ≤6.6% of the daily variability in these behaviors. We conclude that, within the conditions of the collaborating commercial herd, individual animal is the most important factor explaining daily variability in rumination, activity, and lying time.

Beneficiation of Low-Grade Dilband Iron Ore by Reduction Roasting

This research is aimed at the up-gradation of indigenous Pakistani iron ore, i.e., Dilband iron ore (hematite), by utilizing common metallurgical processes. First, the magnetic properties of the ore were determined. Initially, the iron ore samples contained 34 wt. % Fe in addition to other gangue materials. Therefore, the ore was subjected to a high-temperature reduction roasting process between 800 °C and 1000 °C. Additionally, the magnetic separation process was also employed. The influence of different roasting parameters, such as the reduction time, coal-to-ore ratio, and temperature, was examined. This was followed by characterization techniques using XRD (X-ray diffraction analysis), the Rietveld method, wet chemistry analysis, and a VSM (Vibrating Sample Magnetometer). The results suggest an excellent reduction at 900 °C for a coal/ore ratio of 20 wt. %, which was achieved within 2 h of the process. The Fe concentration increased tremendously from 34 to 56 wt. %, and in conjunction, magnetic properties were also induced (1.5 emu). The recovery was found to be substantial for the ore when the Fe content was 75 wt. %. Additionally, the economic feasibility of the processed ore was also studied, followed by an extensive analysis of the roasting and magnetic separation processes.

Reduction in fossil fuel consumption by exploiting separation of refractory grade oolitic iron ores using black plum leaf litter via magnetization roasting & pelletization; a small step toward decarbonization & sustainability

An increase in the annual energy utilization beyond 20% for the iron & steel industries promotes sustainable development through augmented clean technologies. Moreover, the consistent development of the country always depends on its infrastructure growth and improved per capita consumption of steel, which leads to an increase in greenhouse gas(GHG) emissions. Therefore, pondering possible alternative technologies for low-carbon steel production while undisturbing the country's rapid growth is the need of the hour worldwide. The present research work assesses the use of Black plum leaf litters (BPLLs; solid carbonaceous biomass) as an alternative biomass reductant integrating magnetization roasting and pelletization while upgrading the low-grade refractory oolitic iron ore (OIO). The magnetite concentrate generated from oolitic iron ore (41.73% Fe(T), 14.51% SiO2, 8.61% Al2O3, and 12.36% loss on ignition (LOI; contributed mainly from clay and goethite) at 900 ℃, 45 wt% of BPLLs, and 5 min. of residence time have 64.37% Fe(T) with 65.56% yield suitable for pellet feed application. Higher volatile matter (VM:68.09%) in the BPLLs and the internal porosity of the oolitic ore stimulate the magnetization process at a lower roasting temperature. Moreover, the innovative approach of employing BPLLs in producing metallurgical grade pellets (Cold crushing strength(CCS):216.6 kg/pellet, Porosity:27.81%, RDI-2:RDI]-2.8 = 4.48%) from the OIO has the potential benefits in sustainable development and decarbonization. In this regard, more advanced characterization techniques like; X-ray microCT (X-ray microtomography), X-ray diffraction (XRD), Field emission scanning electron microscope (FESEM), and wet-chemical analysis were applied to evaluate the suitability of less carbon-intensive magnetite concentrate and indurated pellet production.