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1H NMR

1H NMR (Proton Nuclear Magnetic Resonance) is a powerful analytical technique used to identify and characterize organic compounds.
It involves the detection of hydrogen (1H) nuclei within a molecule, providing information about the chemical environment and connectivity of these hydrogen atoms. 1H NMR is widely applied in chemistry, biochemistry, and pharmaceutical research to elucidate the structure and purity of compounds, as well as to monitor chemical reactions and study biomolecular interactions.
The technique relies on the magnetic properties of hydrogen nuclei, which can be detected and analyzed using specialized instrumentation. 1H NMR spectra offer a wealth of information, including chemical shift, coupling patterns, and signal intensities, allowing researchers to make informed decisions about the optimal conditions and products for their experiments.
By streamlining the research process with AI-powered tools like PubCompare.ai, scientists can quickly locate the best 1H NMR protocols from literature, pre-prits, and patents, and identify the ideal conditions for their specific applications.

Most cited protocols related to «1H NMR»

Serum samples contain a substantial portion of large molecular weight proteins and lipoproteins, which affects the identification and quantification of small molecule metabolites by NMR spectroscopy. Consequently, we introduced a step in the protocol to remove serum proteins (deproteinization). There are several routes to serum deproteinization, including organic solvent (acetonitrile, methanol, isopropanol) precipitation, ultrafiltration [28] (link), [44] (link) as well as spectral manipulation methods such as diffusion editing [45] (link). While other researchers have found that ultrafiltration yields poor signal-to-noise ratios, we found that by using an ultrafiltration protocol similar to that described by Tiziani [46] (link) and Weljie et al [47] (link), we could obtain excellent spectra that yielded metabolite concentrations that closely matched known values measured using standard clinical chemistry techniques. Ultrafiltration also has other advantages: it is relatively quick, very reproducible, does not introduce unwanted solvent peaks and is “safe” in terms of avoiding unwanted side-reactions with biofluid metabolites. All 1H-NMR spectra were collected on a either a 500 MHz or 800 MHz Inova (Varian Inc., Palo Alto, CA) spectrometer using the first transient of the tnnoesy-presaturation pulse sequence. The resulting 1H-NMR spectra were processed and analyzed using the Chenomx NMR Suite Professional software package version 6.0 (Chenomx Inc., Edmonton, AB), as previously described [15] . Further details on the NMR sample preparation and NMR data acquisition are provided in File S1.
Publication 2011
1H NMR acetonitrile Diffusion Isopropyl Alcohol Lipoproteins Methanol Proteins Pulse Rate Serum Serum Proteins Solvents Spectroscopy, Nuclear Magnetic Resonance Staphylococcal Protein A Transients Ultrafiltration
Due to the expense of sampling and data collection in metabolomics experiments, a tendency exists in metabolic fingerprinting studies to retain all collected variables for multivariate analysis [86 ]. Unfortunately, this exacerbates the aforementioned collinearity problem and increases the likelihood of finding spurious correlations in data, leading to a greater chance of inferring incorrect biological conclusions [46 (link), 51 ]. While it is not typically performed during data exploration, variable selection may be used conservatively in concert with domain knowledge to select only biologically meaningful regions of datasets for classification or dimensionality reduction. 1H NMR datasets, for instance, may contain highly varying signals from solvents, buffers and chemical shift reference compounds, as well as large signal-free noise regions. Both of these features may obscure biologically relevant variation and are good candidates for variable selection/removal. Structured noise (baseline issues, contaminants) negatively affects the correspondence between scores and loadings and hinders the correct interpretation of PLS results [87 ]. OPLS can separate out this structured noise, but often at the expense of an overly complex model [88 ]. There are significantly more variables (K) than observations (N) in a typical metabolomics dataset, so removing irrelevant variables is beneficial to the multivariate analysis. A more exhaustive variable selection approach applies a pretreatment based on orthogonal projections [89 ] or a recursive algorithm [90 -92 (link)], support vector machine, genetic algorithm or random forest, to select for variables or spectral features primarily contributing to class separation. This is particularly pertinent to MS metabolomics data that may contain a very large number of variables, of which only a small percentage is relevant. Importantly, either the complete absence of variable selection or an overly aggressive variable selection may lead to inadequate separation or over-fitting of the dataset, respectively [92 (link), 93 ].
Publication 2013
1H NMR Biopharmaceuticals Buffers Childbirth Solvents
Initial peak assignments relied on established
literature values, specifically, the human metabolome database,17 (link) the biological magnetic resonance data bank,18 (link) and publications from our laboratory on the
serum metabolome.7 (link),19 (link) Unknown metabolite identification
involved a combination of literature/database searches,17 (link) chemical shift, peak multiplicity, and J couplings measurements, and comprehensive 2D DQF-COSY
and TOCSY spectral analyses. The putative new compounds were finally
confirmed by spiking with authentic compounds (see SI Table S1). Chenomx NMR Suite Professional Software package
(version 5.1; Chenomx Inc., Edmonton, Alberta, Canada) was used to
quantitate the metabolites. This software allows fitting spectral
lines using the standard metabolite library for 800 MHz 1H NMR spectra and, in particular, the determination of concentrations
in complicated, overlapped spectral regions. One complication that
arises is that the proximity of chemical shift values for multiple
metabolite signals often result in the software providing multiple
library hits for the same metabolite peak; the correct metabolite
identification therefore relied on the newly established metabolite
identification as annotated for a typical 1H NMR spectrum
(vide infra). Peak-fitting with reference to the internal TSP signal
enabled the determination of absolute concentrations for identified
metabolites in protein-precipitated serum except for 2-oxoisovaleric,
which was absent in the Chenomx library and was therefore quantitated
by manual integration using the Bruker Topspin versions 3.0 or 3.1
software package.
Publication 2014
1H NMR Biopharmaceuticals cDNA Library Homo sapiens Magnetic Resonance Metabolome Serum Proteins
We performed a Monte Carlo simulation using MVAPACK (http://bionmr.unl.edu/mvapack.php, [15 (link)]) to analyze the relationship between group separation in PCA scores-space and OPLS-DA cross-validation metrics as a function of spectral noise content. A data matrix of 32 binned 1D 1H NMR spectra from the freely available Coffees dataset [15 (link)] was used, as it contains highly significant separation between two experimental groups. A second data matrix comparing 50 1D 1H NMR spectra of chemically defined cell growth media was also subjected to Monte Carlo analysis. A set of 50 linearly increasing additive noise points was constructed such that the noise standard deviation increased relative to the intrinsic variation (l2 norm) of the original data matrix. Two hundred Monte Carlo iterations were performed at each noise point, where each iteration had a different sample of Gaussian noise added to the data matrix based on the current noise standard deviation. Within each iteration, a three-component PCA model and an OPLS-DA model with a single predictive component were constructed. Component counts for PCA and OPLS-DA models were forced, rather than determined by cross-validation, to ensure that enough components were available for subsequent computations. For each OPLS-DA model, a CV-ANOVA [16 ] was performed to assess model reliability. In addition, the Mahalanobis distance (DM) [17 ] between groups within PCA scores-space was computed at each iteration to quantify the significance of the group separation. The correlation between the OPLS-DA model loadings and the original noise-free loadings were also computed at each iteration to determine how well the OPLS-DA model reproduced the “true” loadings. Key results of the Monte Carlo analysis are shown in Fig. (1 and 2).
Publication 2016
1H NMR Cells Coffee Culture Media neuro-oncological ventral antigen 2, human
Validation of the proper operation
of the NMR processing functions of MVAPACK was performed by visually
comparing the MVAPACK-processed 1D 1H NMR spectra from
the Coffees data set (Figure 2) with the processed
NMR spectra produced by ACD/1D NMR Manager (Advanced Chemistry Development).
Verification of icoshift alignment
performance was performed using
the Wine 1H NMR data set42 available
from the University of Copenhagen. As this data set contains large
amounts of chemical shift dispersion due to differences in chemical
properties of each wine, it is an ideal basis for assessing the performance
of NMR peak alignment algorithms (Figure 3).
Validation of the proper operation of PCA, PLS and OPLS multivariate
decompositions was performed by comparing the scores produced by analysis
of the Coffees NMR data set in MVAPACK with those produced by SIMCA-P+
13.0 (Umetrics AB, Umea, Sweden) (Figures 4 and 5).
Publication 2014
1H NMR Coffee Wine

Most recents protocols related to «1H NMR»

Example 21

Complex Em9-i:

[Figure (not displayed)]

A solution of 0.17 g of complex Em9-s in 2000 ml acetonitril are irradiated at 15° C. for 9.5 h with a blacklight-blue-lamp (Osram, L18W/73, λmax=370-380 nm). The solvent is removed in vacuo. The residue is purified by chromatography (cyclohexane/acetic ester). 0.055 g of Em9-i (32%, contaminated with traces of a further complex) are obtained as well as 0.075 g of reisolated Em9-s (44%) are reisolated.

1H-NMR [CD2Cl2, 400 MHz, sample comprises traces of a further complex observable for example at 0.77 (m), 0.83 (d), 1.04 (d), 1.21 (m), 1.92 (sept), 2.34 (sept), 7.20-7.23 (m), 7.31-7.34 (m)]:

δ=0.65 (d, 3H), 0.77 (d, 3H), 0.85 (d, 3H), 0.97 (d, 3H), 0.98 (d, 3H), 1.02 (d, 3H), 1.13 (d, 6H), 1.82 (sept, 1H), 2.33 (sept, 1H), 2.54 (sept, 1H), 2.67 (sept, 1H), 3.04 (s, 3H), 6.09 (dd, 2H), 6.37 (td, 1H), 6.40-6.44 (m, 3H), 6.50 (m, 1H), 6.59 (d, 1H), 6.61 (td, 1H), 6.68 (d, 1H), 6.70 (d, 1H), 6.72 (d, 1H), 6.86 (d, 1H), 6.96 (br.s, 1H), 7.14 (me, 2H), 7.20-7.23 (m, 1H), 7.23-7.31 (m, 3H), 7.44-7.50 (m, 3H).

MS (Maldi):

m/e=979 (M+H)+

photoluminescence (in film, 2% in PMMA):

λmax=457, 485 nm, CIE: (0.17; 0.26)

The photoluminescence quantum efficiency of the isomer Em9-i has the 1.14-fold value of the quantum efficiency of the isomer Em9-s.

Patent 2024
1H NMR carbene Chromatography Cyclohexane Esters Isomerism NADH Dehydrogenase Complex 1 Polymethyl Methacrylate Solvents Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Suby's G solution

Example 30

[Figure (not displayed)]

To a stirred solution of 3-(3,4-dimethoxyphenyl)-5-(4-piperidyl)-1,2,4-oxadiazole (150 mg, 518 μmol) in N,N-dimethylformamide (1.50 mL) were added (2-(1H-benzotriazol-1-yl)-1,1,3,3-tetramethyluronium hexafluorophosphate) (196 mg, 518 μmol), N-ethyl-N-(propan-2-yl)propan-2-amine (201 mg, 1.56 mmol, 271 μL), and 2-(benzylamino)acetic acid (89 mg, 544 μmol). The mixture was stirred at 20° C. for 16 h and filtered, and the crude filtrate was purified directly by prep-HPLC (column: Luna C8 100×30 5 μm; mobile phase: [water (10 mM ammonium carbonate)-acetonitrile]; B%: 30%-60%, 12 min) to give 2-(benzylamino)-1-[4-[3-(3,4-dimethoxyphenyl)-1,2,4-oxadiazol-5-yl]-1-piperidyl]ethanone (48 mg, 110 μmol, 21%) as a yellow solid. 1H NMR (400 MHz, METHANOL-d4) δ=7.65 (dd, J=1.8, 8.2 Hz, 1H), 7.57 (d, J=1.8 Hz, 1H), 7.40-7.30 (m, 4H), 7.28-7.22 (m, 1H), 7.06 (d, J=8.4 Hz, 1H), 4.45 (br d, J=13.7 Hz, 1H), 3.94-3.83 (m, 7H), 3.78 (s, 2H), 3.57-3.44 (m, 2H), 3.40-3.33 (m, 1H), 3.27-3.20 (m, 1H), 3.01 (t, J=11.2 Hz, 1H), 2.17 (dd, J=2.8, 13.3 Hz, 2H), 1.93-1.73 (m, 2H); LCMS (ESI) m/z: [M+H]+=437.3.

Patent 2024
1H NMR Acetic Acid acetonitrile Amines ammonium carbonate Dimethylformamide High-Performance Liquid Chromatographies Lincomycin Methanol Oxadiazoles

Example 161

[Figure (not displayed)]

To a solution of 2-(piperazin-1-yl)ethanol (0.73 g, 5.6 mmol, 1 eq.) in DMF (10 mL) was added K2CO3 (1.56 g, 11.3 mmol, 2 eq.) followed by 1,2,4-trifluoro-5-nitrobenzene (1 g, 5.6 mmol, 1 eq.) and the mixture was stirred at 0° C. for 1 hour. The mixture was poured into ice-water (100 mL), extracted by EA (3×40 mL), and the organic layers were combined, washed with brine (150 mL), concentrated and purified via column chromatography (10-95% CH3CN—H2O) to afford 2-(4-(2,5-difluoro-4-nitrophenyl)piperazin-1-yl)ethanol (0.65 g, 41%) as a yellow solid.

[Figure (not displayed)]

To a solution of 2-(4-(2,5-difluoro-4-nitrophenyl)piperazin-1-yl)ethanol (0.65 g, 2.3 mmol) in MeOH (50 mL) was added Pd/C (100 mg) and the resulting mixture was stirred at r.t. overnight. The Pd/C was removed by filtration and the filtrate was concentrated to afford 2-(4-(4-amino-2,5-difluorophenyl)piperazin-1-yl)ethanol (0.58 g, 99%).

[Figure (not displayed)]

To a suspension of 2-(4-(4-amino-2,5-difluorophenyl)piperazin-1-yl)ethanol (270 mg, 0.88 mmol, 1 eq.) and N-(3-(2-chloroquinazolin-8-yl)phenyl)acrylamide (225 mg, 0.88 mmol, 1 eq.) in n-BuOH (10 mL) was added TFA (0.5 mL, 4.4 mmol, 5 eq.) and the resulting mixture was stirred at 90° C. overnight. The mixture was concentrated, diluted with DCM (20 mL), washed with Na2CO3 solution (20 mL), dried, concentrated and purified via column chromatography (DCM/MeOH=10/1) to afford N-(3-(2-((2,5-difluoro-4-(4-(2-hydroxyethyl)piperazin-1-yl)phenyl)amino)quinazolin-8-yl)phenyl)acrylamide (120 mg, 26%) as yellow solid. LRMS (M+H+) m/z calculated 531.2, found 531.2. 1H NMR (DMSO-d6, 400 MHz) δ 10.18 (s, 1H), 9.37 (s, 1H), 9.17 (s, 1H), 7.97-7.94 (m, 3H), 7.83-7.74 (m, 2H), 7.50-7.39 (m, 3H), 6.90-6.85 (m, 1H), 6.48-6.41 (m, 1H), 6.23 (dd, 1H), 5.73 (dd, 1H), 4.42 (t, 1H), 3.55-3.50 (m, 2H), 2.94-2.91 (m, 4H), 2.55-2.54 (m, 4H), 2.44 (t, 2H).

Patent 2024
1H NMR Acrylamide brine Chromatography Ethanol Filtration Ice Nitrobenzenes Piperazine potassium carbonate Sulfoxide, Dimethyl
Not available on PMC !

Example 1

InCl (1 eq.) was added to a Schlenk flask charged with LiCp(CH2)3NMe2 (11 mmol) in Et2O (50 mL). The reaction mixture was stirred overnight at room temperature. After filtration of the reaction mixture, the solvent was evaporated under reduced pressure to obtain a red oil. After distillation a yellow liquid final product was collected (mp˜5° C.). Various measurements were done to the final product. 1H NMR (C6D6, 400 MHz): δ 5.94 (t, 2H, Cp-H), 5.82 (t, 2H, Cp-H), 2.52 (t, 2H, N—CH2—), 2.21 (t, 2H, Cp-CH2—), 2.09 (s, 6H, N(CH3)2, 1.68 (q, 2H, C—CH2—C). Thermogravimetric (TG) measurement was carried out under the following measurement conditions: sample weight: 22.35 mg, atmosphere: N2 at 1 atm, and rate of temperature increase: 10.0° C./min. 97.2% of the compound mass had evaporated up to 250° C. (Residue <2.8%). T (50%)=208° C. Vacuum TG measurement was carried out under delivery conditions, under the following measurement conditions: sample weight: 5.46 mg, atmosphere: N2 at 20 mbar, and rate of temperature increase: 10.0° C./min. TG measurement was carried out under delivery conditions into the reactor (about 20 mbar). 50% of the sample mass is evaporated at 111° C.

Using In(Cp(CH2)3NMe2) synthesized in Example 1 as an indium precursor and H2O and O3 as reaction gases, indium oxide film may be formed on a substrate by ALD method under the following deposition conditions. First step, a cylinder filled with In(Cp(CH2)3NMe2) is heated to 90° C., bubbled with 100 sccm of N2 gas and the In(Cp(CH2)3NMe2) is introduced into a reaction chamber (pulse A). Next step, O3 generated by an ozone generator is supplied with 50 sccm of N2 gas and introduced into the reaction chamber (pulse B). Following each step, a 4 second purge step using 200 sccm of N2 as a purge gas was performed to the reaction chamber. 200 cycles were performed on a Si substrate having a substrate temperature of 150° C. in the reaction chamber at a pressure of about 1 torr. As a result, an indium oxide film will be obtained at approximately 150° C.

Example 2

Same procedure as Example 1 started from Li(CpPiPr2) was performed to synthesize In(CpPiPr2). An orange liquid was obtained. 1H NMR (C6D6, 400 MHz): δ 6.17 (t, 2H, Cp-H), 5.99 (t, 2H, Cp-H), 1.91 (sept, 2H, P—CH—), 1.20-1.00 (m, 12H, C—CH3).

Using In(CpPiPr2) synthesized in Example 2 as the indium precursor and H2O and O3 as the reaction gases, indium oxide film may be formed on a substrate by the ALD method under the following deposition conditions. First step, a cylinder filled with In(CpPiPr2) is heated to 90° C., bubbled with 100 sccm of N2 gas and the In(CpPiPr2) is introduced into a reaction chamber (pulse A). Next step, O3 generated by an ozone generator is supplied with 50 sccm of N2 gas and introduced into the reaction chamber (pulse B). Following each step, a 4 second purge step using 200 sccm of N2 as a purge gas was performed to the reaction chamber. 200 cycles were performed on the Si substrate having a substrate temperature of 150° C. in an ALD chamber at a pressure of about 1 torr. As a result, an indium oxide was obtained at 150° C.

Patent 2024
1H NMR Atmosphere Distillation Fever Filtration Indium indium oxide Obstetric Delivery Ozone Pressure Pulse Rate Solvents Vacuum

Example 1

10 g (33.09 mmol) of 1-(2-fluoro-6-trifluoromethyl-benzyl)-6-methyl-1H-pyrimidine-2,4-dione (III), 6.8 g (49.62 mmol) of K2CO3 and 2.4 g (6.6 mmol) of tetrabutylammonium iodide were mixed with 50 mL of acetone at the temperature of about 20° C. Subsequently, 13.6 g (43.12 mmol) of (R)-2-((tert-butoxycarbonyl)amino)-2-phenylethyl methanesulfonate (IVa) were added and the obtained mixture was heated at the temperature of about 55° C. and maintained under stirring for about 16 hours at said temperature.

Once this maintenance was finished, the solvent was vacuum distilled and 50 mL of ethyl acetate and 50 mL of water were added to the residue thus obtained. A 1 M aqueous solution of HCl was slowly added, maintaining the temperature between 20 and 25° C. until achieving a pH of between 7 and 8. The aqueous phase was separated and treated with 3 fractions of 30 mL each of ethyl acetate. All the organic extracts were pooled and the solvent was removed by means of vacuum to obtain a slightly yellowish oily residue to which 45 mL of methanol were added, obtaining complete dissolution of the residue.

Example 2

16.1 g (99.24 mmol) of iodine monochloride (ICI) were dissolved in 40 mL of methanol at the temperature of about 10° C. The methanol solution previously obtained according to the methodology described in Example 1 comprising 3-((R)-2-(tert-butoxycarbonyl)amino-2-phenylethyl)-1-(2-fluoro-6-trifluoromethylbenzyl)-6-methyl-1H-pyrimidine-2,4-dione (II) was added to the iodine monochloride solution, maintaining the temperature between 20 and 25° C. Once the addition was finished, the obtained solution was heated to about 50° C. and was maintained under stirring for 2 hours at the mentioned temperature.

Once the maintenance was finished, the solvent was vacuum distilled and 50 mL of acetone were slowly added to the obtained oily residue at the temperature of between and 25° C. The addition of acetone caused a solid precipitate to appear almost immediately. The obtained mixture was maintained for 1 hour under stirring at the mentioned temperature. The resulting solid was isolated by filtration, washed with two fractions of 25 mL of acetone, and finally dried at the temperature of 50° C. to obtain 15.6 g (80.8% yield) of a white solid corresponding to the 3-((R)-2-(amino-2-phenylethyl)-1-(2-fluoro-6-trifluoromethylbenzyl)-5-iodo-6-methyl-1H-pyrimidine-2,4-dione hydrochloride salt (Ia) (UHPLC purity: 98.9%).

1H-NMR (d6-DMSO, 400 MHz) δ (ppm): 8.70 (2H, s broad), 7.65-7.48 (3H, m), 7.40-7.32 (5H, m), 5.40-5.29 (2H, dd), 4.47 (1H, t), 4.25 (2H, dd), 2.65 (3H, s).

13C-NMR (d6-DMSO, 100 MHz) δ (ppm): 161.87, 159.47, 159.41, 154.19, 150.98, 134.70, 129.93, 129.84, 129.01, 128.58, 127.38, 122.61, 122.34, 122.22, 121.34, 121.10, 74.80, 52.26, 45.45, 44.60, 25.66.

The DSC of this compound is shown in FIG. 1 and the XRPD is shown in FIG. 2.

Patent 2024
1H NMR Acetone Anabolism Carbon-13 Magnetic Resonance Spectroscopy elagolix ethyl acetate Filtration Iodine iodine monochloride methanesulfonate Methanol Oils potassium carbonate Pyrimidines Sodium Chloride Solvents Sulfoxide, Dimethyl TERT protein, human tetrabutylammonium iodide Vacuum

Top products related to «1H NMR»

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Topspin 3.2 is a software package developed by Bruker for the acquisition, processing, and analysis of nuclear magnetic resonance (NMR) data. It provides a comprehensive suite of tools for the management and interpretation of NMR spectra.
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More about "1H NMR"

1H nuclear magnetic resonance (1H NMR) is a powerful analytical technique widely used in chemistry, biochemistry, and pharmaceutical research to identify and characterize organic compounds.
This non-destructive method relies on the magnetic properties of hydrogen nuclei, providing valuable information about the chemical environment and connectivity of these atoms within a molecule. 1H NMR spectroscopy offers a wealth of data, including chemical shift, coupling patterns, and signal intensities, which researchers can use to elucidate the structure and purity of compounds, as well as monitor chemical reactions and study biomolecular interactions.
The technique is often employed in conjunction with other analytical tools, such as 13C NMR, mass spectrometry, and infrared spectroscopy, to obtain a comprehensive understanding of a sample.
The 1H NMR process involves placing a sample in a strong magnetic field generated by specialized instrumentation, such as the Avance III, Avance 400, or AV-400 spectrometers.
The hydrogen nuclei within the sample align with the magnetic field, and when exposed to a specific radio frequency, they absorb and re-emit energy, producing a spectrum that can be analyzed.
To prepare samples for 1H NMR analysis, common techniques include dissolving the compound in deuterated solvents, such as chloroform-d or dimethyl sulfoxide-d6, and using silica gel 60 or XBridge C18 columns for purification.
The resulting 1H NMR spectra can be processed and interpreted using software like Topspin 3.2, providing researchers with valuable insights into the structure and purity of their samples.
By leveraging AI-powered tools like PubCompare.ai, scientists can streamline their 1H NMR research by quickly locating the best protocols from literature, pre-prInts, and patents, and identifying the optimal conditions for their specific applications.
This helps to accelerate the research process and ensure the efficient use of resources.