The largest database of trusted experimental protocols

Lead nitrate

Lead nitrate is an inorganic compound with the chemical formula Pb(NO3)2.
It is a white, crystalline solid that is soluble in water and commonly used in various industrial and research applications.
Lead nitrate has a wide range of uses, including as a pigment in paints, a mordant in textile dyeing, and a reagent in chemical synthesis.
In the context of biological research, lead nitrate may be used as a model compound to study the effects of lead exposure on living organisms.
Researchers can utilize PubCompare.ai's AI-driven comparison tool to identify the most reproducible and accurate protocols from the literature, pre-printe, and patents for optimizing lead nitrate-related studies, empowering their research to reach new heights.
Experinece the future of protocol optimization today with PubCompare.ai.

Most cited protocols related to «Lead nitrate»

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2014
1,1'-((4,4,7,7-tetramethyl)-4,7-diazaundecamethylene)bis-4-(3-methyl-2,3-dihydro(benzo-1,3-oxazole)-2-methylidine)quinolinium, tetraiodide Cells Comet Assay DAPI DNA Damage EDNRB protein, human Fluorescent Dyes Gold Head Light Propidium Iodide Silver Nitrate SYBR Green I Tail
Large-volume en-bloc staining was performed as follows (also see Supplementary Table 1 for details). Tissue was first immersed in 2% OsO4 aqueous solution (Serva) buffered with cacodylate (0.15 M, pH 7.4) at room temperature for 90 min. The staining buffer was then replaced by 2.5% ferrocyanide (Sigma-Aldrich) in 0.15 M cacodylate buffer (pH 7.4) and incubated at room temperature for another 90 min. Sequentially, the tissue was incubated in filtered thiocarbohydrazide (saturated aqueous solution at room temperature, Sigma-Aldrich) at 40 °C for 45 min, 2% unbuffered OsO4 aqueous solution at room temperature for 90 min and 1% UA (Serva) aqueous solution at 4 °C overnight. Double rinses in nanopure filtered water for 30 min each were performed between the ferrocyanide and thiocarbohydrazide step, the thiocarbohydrazide and OsO4 step, and the OsO4 and UA step. On the next day, the tissue (still in UA solution) was warmed up to 50 °C (oven) for 120 min. After being washed twice in nanopure filtered water at room temperature for 30 min, the tissue was incubated in a lead aspartate solution at 50 °C for 120 min. The lead aspartate solution was prepared by dissolving 0.066 g lead nitrate (Sigma-Aldrich) in 10 ml 0.03 M aspartic acid (Serva) and pH adjusted to 5 with 1 N KOH. The tissue was then washed twice in nanopure filtered water for 30 min. The image in Supplementary Fig. 2b was taken from a tissue block stained with the same procedure described above except that the 120 min 50 °C incubation in UA was omitted.
Full text: Click here
Publication 2015
Aspartate Aspartic Acid Buffers Cacodylate hexacyanoferrate II lead nitrate Strains thiocarbohydrazide Tissues
We used the data and modeling strategy applied by Strickland et al.,22 (link) building upon previous results by considering joint effects of pollutant combinations. Methods for the original study are described in detail elsewhere.22 (link) Briefly, daily concentrations of ambient 1-hour maximum carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2); 8-hour maximum ozone (O3); and 24-hour average PM2.5 and the PM2.5 components elemental carbon (EC), sulfate (SO42−), nitrate (NO3) and ammonium (NH4+) for the period August 1, 1998 through December 31, 2004 were obtained from several ambient monitoring networks in Atlanta.23 (link),24 (link) Daily pollutant measurements across monitors were combined using population weighting.25 (link) Individual-level data on ED visits during this time period were obtained from metropolitan Atlanta hospitals. Using International Classification of Diseases, 9th Revision (ICD-9), pediatric asthma ED visits were defined as visits with a code for asthma (493.0–493.9) or wheeze (786.09 before October 1, 1998; 786.07 after October 1, 1998) that did not have a code for an external injury or poisoning (E800-E999) among children aged 5 to 17 years. ED visits for acute upper respiratory infections (codes 460.0–466.0) among children in the same age group who did not also have a code for asthma or wheeze were also identified.
We examined the joint effects of combinations of commonly measured pollutants, selected to represent oxidant gases, secondary pollutants, pollutants from traffic and coal-fired power plant sources, and criteria pollutants (Table 1). The “oxidant gases” combination included the major gaseous oxidant air pollutants (O3, NO2 and SO2). The “secondary” pollutant combination included O3 and secondary PM2.5 (for which the concentration was calculated as the sum of the concentrations of the major inorganic PM2.5 components SO42−, NO3, and NH4+). The pollutants in the “traffic” combination were those most closely associated with traffic pollution in Atlanta (CO, NO2 and EC).10 (link) Organic carbon (OC) measurements were not used in these analyses because OC originates from both primary (e.g. traffic) and secondary sources, and speciated organic data needed to apportion OC to sources were available for only a limited time period. The pollutants in the “power plant” combination (SO2 and SO42−) were selected to be relatively (although not completely) specific for pollution from coal-fired power plants.26 (link),27 (link) The “criteria pollutants” combination included five of the pollutants for which NAAQS are set (including O3, CO, NO2, SO2 and PM2.5; excluding lead and particulate matter less than 10 μm in diameter). The “criteria pollutants” combination was included because of its potential regulatory significance.
We examined joint effects of pollutant combinations on pediatric asthma ED visits using Poisson generalized linear models that accounted for overdispersion. Because previous analyses showed different warm and cold-season effects,22 (link) analyses were season-specific. We used the same general model form as Strickland et al.22 (link) The dependent variable was the hospital-specific daily pediatric asthma ED visit count. For each pollutant in a given combination, the primary models included a linear term for the three-day moving average of pollutant concentrations [the average of the pollutant concentration on a given day (lag 0) and the previous two days (lags 1 and 2)]. Models also included a linear term for the logarithm of the daily non-asthma pediatric ED visit count for acute upper respiratory infections; cubic polynomials for day-of-season, the moving average of dew point (lags 0–2) and the moving average of minimum temperature (lags 1 and 2); indicator variables for year, month, day of week or holiday (with holidays having a separate indicator), hospital and same-day (lag 0) maximum temperature (for each degree Celsius); and interaction terms between month and year, month and lag 0 maximum temperature, and month and day of week. We estimated joint effects for an interquartile-range (IQR) increment in the three-day moving average of each pollutant concentration as the exponentiated sum (across the pollutants in the combination) of the product of each pollutant’s model coefficient and that pollutant’s IQR. Standard errors for joint effects were calculated using the variance-covariance matrices for the individual-pollutant coefficients (for a mathematical representation of models and joint effect calculations, see Supplemental Material, Model Details).
Initial models included only the pollutants in the specified combination, without interaction terms. The results of these models were compared with the results of models that also included linear terms for all first-order multiplicative interactions between the pollutants in the combination (e.g., for the oxidant gases combination, models included terms for O3, NO2, SO2, and the product terms O3*NO2, O3*SO2 and NO2*SO2). In sensitivity analyses, we considered models with linear, quadratic and cubic terms for each pollutant, to assess the adequacy of using linear pollutant terms. In interaction models and models with quadratic and cubic terms, joint effects were calculated for a concentration change equal in magnitude to each pollutant’s IQR, starting from each pollutant’s 15th, 25th, or 35th percentile levels. We also considered models that controlled for other potentially confounding pollutants.
Concurvity (the analogue of collinearity in non-linear models) was calculated for each pollutant as the correlation between observed pollutant concentrations and predicted concentrations from linear models including all other model variables (i.e., other pollutant terms, time variables and meteorological variables).28 (link) We also calculated concurvity for each pollutant in relation to only the other pollutant terms in the models (without time and meteorological variables).
Publication 2014
Birth data. Birth certificate data for Connecticut, Delaware, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, Virginia, Washington, DC, and West Virginia (USA), from 1 January 2000 through 31 December 2007 were obtained from the National Center for Health Statistics (Atlanta, GA). Data that were provided include county of residence, county of birth, birth order, trimester of first prenatal care, date of last menstrual period (LMP), gestational age, infant’s sex and birth weight, as well as maternal and paternal ages and races, and maternal education, marital status, alcohol consumption, and smoking during pregnancy. Further description of these data is available elsewhere (Bell et al. 2007b (link)).
Births with unspecified county of residence or birth, plural deliveries (e.g., twins), gestational period > 44 weeks, gestational period < 37 weeks (nonterm births), birth weight < 1,000 g or > 5,500 g, different counties of residence and delivery, or impossible gestational age and birth weight combinations were excluded from analysis (Alexander et al. 1996 (link)). Births also were excluded if LMP was missing or the estimated birth based on LMP and gestational length was > 30 days from the midday of the birth month reported on the birth certificate.
Air pollution and weather data. PM2.5 chemical components data were obtained from the U.S. Environmental Protection Agency (EPA) Air Explorer (U.S. EPA 2010a). PM10 and PM2.5 total mass, CO, NO2, O3, and SO2 data were obtained from the U.S. EPA Air Quality System for 1999–2007 (U.S. EPA 2010b). We included only counties with PM2.5 chemical component data because these exposures are our primary focus. PM10, PM2.5, and PM2.5 chemical components were measured every 3–6 days. Gaseous pollutants were measured daily, although O3 was measured mainly during the warm season. Some monitors began or ceased observation during the study period. We investigated PM2.5 chemical components identified by previous research and literature review to have potential links to health and/or contribute substantially to PM2.5 total mass: aluminum, ammonium ion, arsenic, cadmium, calcium, chlorine, elemental carbon, lead, mercury, nickel, nitrate, organic carbon matter, silicon, sodium ion, sulfur, titanium, vanadium, and zinc (Bell et al. 2007a (link); Franklin et al. 2008 (link); Haynes et al. 2011 (link); Ostro et al. 2007 (link); Zanobetti et al. 2009 (link)).
We calculated apparent temperature (AT), a measure that reflects overall temperature discomfort (Kalkstein and Valimont 1986 ), based on daily temperature and dew point temperature data obtained from the National Climatic Data Center (2010) . If weather data were unavailable for a given county, we assigned the AT value for the closest county with weather data.
Exposure estimation. For each birth we calculated the average level of each pollutant during gestation and each trimester, and average AT during each trimester. Delivery date was estimated based on self-reported LMP and gestational length, assuming conception 2 weeks after LMP. We defined trimesters as 1–13 weeks, 14–26 weeks, and week 27 to delivery, as in previous studies (Bell et al. 2007b (link)).
Exposures were estimated based on county of residence. Not all counties had data for all pollutants. Measurements from multiple monitors in the same county on the same day were averaged to generate daily pollutant -levels. To avoid biases due to changes in measurement frequency, daily pollutant levels and AT values were combined to estimate weekly exposures, which were then averaged to estimate gestational or trimester exposure. Births for which exposure estimates were unavailable for > 25% of the weeks in any trimester for a given pollutant were excluded from analyses of that pollutant.
Statistical analysis. Each birth was cate-gorized as low or normal birth weight using clinically defined LBW (< 2,500 g). Logistic regression was used to estimate associations between LBW and gestational exposure to each pollutant with adjustment for maternal race (African American, Caucasian, other), marital status (married, unmarried), tobacco consumption during pregnancy (yes, no, unknown), alcohol consumption during pregnancy (yes, no, unknown), highest education (< 12 years, 12 years, 13–15 years, > 15 years, unknown), age (< 20, 20–24, 25–29, 30–34, 35–39, ≥ 40 years), infant sex (male, female), gestational length (37–38, 39–40, 41–42, 43–44 weeks), the trimester prenatal care began (1st, 2nd, 3rd, no care, unknown), first in birth order (yes, no, unknown), delivery method (vaginal, cesarean section, unknown), average AT for each trimester, season of birth, and year of birth. In addition we included regional indicators to adjust for local factors such as area-level socioeconomic conditions (Table 1). We conducted sensitivity analyses restricted to first births to assess the influence of multiple births by the same mother on associations (Zhu et al. 1999 (link)).
For pollutants showing statistically significant associations with LBW in single--pollutant models, we conducted two-pollutant models that included pairs of pollutants that were not highly correlated (correlation < 0.5). Similarly, for pollutants associated in single-pollutant model, we assessed effects by trimester using a model with trimesters’ exposures included simultaneously. Because trimester exposures could be correlated, we performed sensitivity analysis with trimester exposures adjusted to be orthogonal using a method we published previously (Bell et al. 2007b (link)). In brief, we predicted exposures of two trimesters using exposure level of a given trimester (reference trimester), calculated their residuals, and put them into models besides exposure of reference trimester. This approach can avoid covariance among trimester exposures. This procedure was repeated using each trimester as the reference trimester, and we have four models for trimester analysis in total (main model and three models as sensitivity -analyses). Further description of this approach is available elsewhere (Bell et al. 2007b (link)).
Additional analyses were conducted for pollutants that showed statistically robust results in two-pollutant models. We included interaction terms between gestational pollutant exposures and sex or race to investigate whether some populations are particularly susceptible, because previous analysis found higher relative risks associated with ambient air pollution in some populations than in others (Bell et al. 2007b (link)). Statistical significance was determined at an alpha level of 0.05 for the entire analyses.
Full text: Click here
Publication 2012

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2009
Acetic Acid Biologic Preservation Buffers Cytochromes c Dihydrolipoamide Dehydrogenase Electron Transport Complex III Electrophoresis Enzyme Assays Enzymes Glycine Hydrolysis lead nitrate lead phosphate Magnesium Chloride Methanol Methylphenazonium Methosulfate Mitochondria NADH NADH Dehydrogenase Complex 1 oligomycin sensitivity-conferring protein Oxidase, Cytochrome-c Phosphates potassium phosphate SDHD protein, human Sodium sodium phosphate Staining Succinate Sulfoxide, Dimethyl Tromethamine

Most recents protocols related to «Lead nitrate»

In experimental exposure in the lab, Two hundred and forty Nile tilapia (30 ± 5 g b.w.) were distributed into glass aquaria containing 10% LC50 of lead nitrate (PbNO3) [34 (link)] and zinc sulfate (ZnSO4) [35 ], 14.33 mg/L and 6.398 mg/L, respectively, for 4, 6, and 8 weeks.
Full text: Click here
Publication 2024

Example 5

This example illustrates the bioavailability to B. licheniformis strain ECOBIO_2 of lead in both chelated and non-chelated forms.

In order to evaluate the bioavailability of Pb(C2H3O2)2 and Pb(NO3)2 in chelated forms with di-sodium EDTA (C10H14N2Na2O8) in 1:2 millimolar ratio (Pb2+:EDTA), stock solution of both the Pb′ salts 20 mM in 100 ml (650.58 mg/100 ml and 662.4 mg/100 ml for lead acetate and lead nitrate respectively) and EDTA 40 mM/100 ml (1.169 g/100 ml) were prepared in pre-acid washed, clean and dried reagent bottles with distilled water and sterilized. Experiments were performed in duplicates. 250 ml of sterilized LBP broth was used as a microbial growth medium, to which 1:2 mM of [Pb(C2H3O2)2:EDTA], 1 mM of Pb(C2H3O2)2, 1:2 mM of [Pb(NO3)2:EDTA] and 2 mM of EDTA alone was amended to the culture flask in the laminar chamber aseptically and inoculated with aforementioned concentration of ECOBIO_2 cells. Culture flasks were observed for precipitation.

To ensure the solubility of metal in the LBP culture medium (table 1), the lead acceptability of B. licheniformis strain ECOBIO_2 was tested. Sodium EDTA was used to chelate the two lead salts like lead acetate and lead nitrate individually. Results are shown in FIGS. 5A through 5E. FIGS. 5A and 5C show the effect of chelated lead salts on bioavailability to B. licheniformis strain ECOBIO_2 under in vitro conditions. FIG. 5B shows the effect of non-chelated lead salts (lead nitrate and lead acetate) on bioavailability to B. licheniformis strain ECOBIO_2 under in vitro conditions. FIG. 5D shows cells growing in the presence of EDTA. FIG. 5E shows Petri plates with precipitate and control culture agar plates. FIG. 5F shows flasks inoculated with heat killed cells (left) and viable cells (right). Marked precipitation was observed only in the flasks with lead nitrate and lead acetate (FIG. 5B), and chelated lead acetate—EDTA (FIG. 5A), but not the lead nitrate complexed with EDTA (FIG. 5C). This shows the involvement of metal speciation in bioavailability of chelated metals.

Full text: Click here
Patent 2024
All chemicals, lead nitrate (Pb(NO3)2), Zinc nitrate (Zn(NO3)2), Cadmium nitrate (Cd(NO3)2), Nickel nitrate (Ni(NO3)2), potassium permanganate (KMnO4), Hydrogen Peroxide (H2O2), Sodium hydroxide (NaOH), nitric acid (HNO3), Zinc sulfate heptahydrate (ZnSO4.7H2O), and potassium hydroxide (KOH) were provided by Merck company (Germany) and used without any further purification.
Full text: Click here
Publication 2024
The reagents used in this study, including hydrogen peroxide (H2O2), nitric acid (HNO3), and sodium nitrate (NaNO3), were purchased from the Guangzhou Chemical Reagent Factory. Magnesium chloride hexahydrate (MgCl2·6H2O), cadmium nitrate tetrahydrate (Cd(NO3)2·4H2O), and lead nitrate (Pb(NO3)2) were purchased from Macklin Reagent Company.
Full text: Click here
Publication 2024
3-Aminopropyltriethoxysilane (APTES, C9H23NO3Si, 99%) and tetraethyl ortho
silicate (TEOS, C8H20O4Si, 98%) were
bought from Macklin Reagent (Shanghai, China). Iron(III) nitrate nanohydrate,
cobalt nitrate hexahydrate, sodium hydroxide (NaOH), ammonia–water
(NH4OH), lead(II) nitrate, zinc acetate, copper(II) nitrate
trihydrate, and ethanol were supplied by Merck. All of the chemicals
utilized in this investigation were of analytical reagent grade.
Publication 2024

Top products related to «Lead nitrate»

Sourced in United States, Germany, India, Japan, Sao Tome and Principe
Lead nitrate is a white crystalline compound with the chemical formula Pb(NO3)2. It is a versatile laboratory reagent used in various applications, including as a precipitating agent, a source of lead ions, and in the synthesis of other lead compounds.
Sourced in United States, Germany
Lead(II) nitrate is an inorganic chemical compound with the formula Pb(NO3)2. It is a colorless, crystalline solid that is soluble in water and various organic solvents. Lead(II) nitrate is commonly used as a laboratory reagent and in various industrial applications.
Sourced in Germany, United States, India, United Kingdom, Italy, China, Spain, France, Australia, Canada, Poland, Switzerland, Singapore, Belgium, Sao Tome and Principe, Ireland, Sweden, Brazil, Israel, Mexico, Macao, Chile, Japan, Hungary, Malaysia, Denmark, Portugal, Indonesia, Netherlands, Czechia, Finland, Austria, Romania, Pakistan, Cameroon, Egypt, Greece, Bulgaria, Norway, Colombia, New Zealand, Lithuania
Sodium hydroxide is a chemical compound with the formula NaOH. It is a white, odorless, crystalline solid that is highly soluble in water and is a strong base. It is commonly used in various laboratory applications as a reagent.
Sourced in Germany, United States, United Kingdom, India, Italy, France, Spain, Australia, China, Poland, Switzerland, Canada, Ireland, Japan, Singapore, Sao Tome and Principe, Malaysia, Brazil, Hungary, Chile, Belgium, Denmark, Macao, Mexico, Sweden, Indonesia, Romania, Czechia, Egypt, Austria, Portugal, Netherlands, Greece, Panama, Kenya, Finland, Israel, Hong Kong, New Zealand, Norway
Hydrochloric acid is a commonly used laboratory reagent. It is a clear, colorless, and highly corrosive liquid with a pungent odor. Hydrochloric acid is an aqueous solution of hydrogen chloride gas.
Sourced in Germany, United States, Italy, United Kingdom, India, France, Spain, Poland, Australia, Belgium, China, Japan, Ireland, Chile, Singapore, Sweden, Israel
Nitric acid is a highly corrosive, strong mineral acid used in various industrial and laboratory applications. It is a colorless to slightly yellow liquid with a pungent odor. Nitric acid is a powerful oxidizing agent and is commonly used in the production of fertilizers, explosives, and other chemical intermediates.
Sourced in United States, Germany, India, United Kingdom, Italy, China, Poland, France, Spain, Sao Tome and Principe, Mexico, Brazil, Japan, Belgium, Singapore, Australia, Canada, Switzerland
Silver nitrate is a chemical compound with the formula AgNO3. It is a colorless, water-soluble salt that is used in various laboratory applications.
Sourced in United States, Germany, United Kingdom, Italy, India, China, France, Spain, Switzerland, Poland, Sao Tome and Principe, Australia, Canada, Ireland, Czechia, Brazil, Sweden, Belgium, Japan, Hungary, Mexico, Malaysia, Macao, Portugal, Netherlands, Finland, Romania, Thailand, Argentina, Singapore, Egypt, Austria, New Zealand, Bangladesh
Acetic acid is a colorless, vinegar-like liquid chemical compound. It is a commonly used laboratory reagent with the molecular formula CH3COOH. Acetic acid serves as a solvent, a pH adjuster, and a reactant in various chemical processes.
Sourced in Germany, United States, United Kingdom, Italy, India, France, China, Australia, Spain, Canada, Switzerland, Japan, Brazil, Poland, Sao Tome and Principe, Singapore, Chile, Malaysia, Belgium, Macao, Mexico, Ireland, Sweden, Indonesia, Pakistan, Romania, Czechia, Denmark, Hungary, Egypt, Israel, Portugal, Taiwan, Province of China, Austria, Thailand
Ethanol is a clear, colorless liquid chemical compound commonly used in laboratory settings. It is a key component in various scientific applications, serving as a solvent, disinfectant, and fuel source. Ethanol has a molecular formula of C2H6O and a range of industrial and research uses.
Sourced in Germany, United States, India
Cadmium nitrate is an inorganic compound with the chemical formula Cd(NO3)2. It is a crystalline salt that is soluble in water and organic solvents. Cadmium nitrate is commonly used in various laboratory applications, including as a reagent in chemical analysis and synthesis.
Sourced in China
Lead nitrate is a chemical compound with the formula Pb(NO3)2. It is a colorless, crystalline solid that is soluble in water. Lead nitrate is used in various industrial and laboratory applications, such as mordant in textile dyeing, an oxidizing agent, and a component in some types of explosives. Its core function is as a chemical reagent in analytical and research applications.

More about "Lead nitrate"