JEOL Resources

Polymeric Materials Analysis by JEOL Mass Spectrometers - A Guidebook

Mon, 20 Oct 2025 16:54:39 GMT

Mass spectrometry (MS) is a method that can perform qualitative and quantitative analyses of polymers and additives contained in polymeric materials by ionizing them and analyzing their molecular masses. There are multiple ionization methods, mass spectrometer types, and data analysis technologies, and it is possible to obtain more comprehensive information by combining them.

In this guidebook, we will introduce polymeric materials analysis solutions that make full use of JEOL's high-performance mass spectrometers.

JMS-S3000 SpiralTOF™ series Polymeric Materials Applications Notebook 2024

Mon, 13 Oct 2025 17:58:34 GMT

Edition September 2024

This is a compendium of mass spectrometry imaging applications notes and JEOL News articles based on the data acquired on JEOL ultra-high mass-resolution MALDI-TOFMS JMS-S3000 SpiralTOF™ series.

Introduction and Fundamentals　P1〜

Development of JMS-S3000: MALDI-TOF/TOF Utilizing a Spiral Ion Trajectory
(Takaya Satoh, JEOL News, 45, 34-37, 2010)
Development of peak extraction method from a high-resolution MALDI-TOF mass spectrum by machine learning focusing on peak shape, and an application to synthetic polymer analysis (MSTips No. 352)
The Relationship between Crystal Condition and Mass Resolving Power, Mass Accuracy (MSTips No. 206)

Polymers / Oligomers　P15〜

Assessing UV Degradation of Polymers: A Study of Polyethylene Terephthalate by using MALDI-TOFMS and GC-TOFMS
EO/PO composition ratio analysis of EO-PO copolymer using JMS-S3000 "SpiralTOF™-plus3.0" and "msRepeatFinder" (MSTips No. 471)
Structural analysis of EO-PO copolymers using high-resolution MALDI-TOFMS and NMR (MSTips No. 423)
Differential analysis of UV degradaed polyethylene terephthalate using JMS-S3000 “SpiralTOF™-plus2.0” and “msRepeatFinder” (MSTips No. 422)
Structural analysis of polyethylene terephthalates with different crystallinity using JMS-S3000 “SpiralTOF™-plus 2.0” (MSTips No. 407)
End group analysis of poly(methyl methacrylate) using MALDI-TOFMS and GC-TOFMS (MSTips No. 404)
Analysis of mPEG5K-Phosphate using JMS-S3000 "SpiralTOF™-plus 2.0" (MSTips No. 402)
Composition analysis of EO-PO copolymers using JMS-S3000 “SpiralTOF™-plus2.0” and “msRepeatFinder V6” (MSTips No. 399)
Elemental Composition Determination of Polymer End Groups Using Accurate Mass (MSTips No. 357)
Structural analysis of anionic surfactants in MALDI negative ion mode using "SpiralTOF™-plus" (MSTips No. 333)
Analysis of degraded polymethyl methacrylate by UV irradiation using high-resolution MALDI-TOFMS and pyrolysis-GC-QMS (MSTips No. 324)
Analysis of degraded polystyrene by UV irradiation using high-resolution MALDI-TOFMS and pyrolysis-GC-QMS (MSTips No. 322)
Structural analysis of polyethylene terephthalate combining an on-plate alkaline degradation method and tandem time-of-flight mass spectrometry (MSTips No. 311)
“Fraction base” KMD plots for a high molecular weight poly(3-hydroxybutyrate-co-3-hydroxyvalerate) copolyester following its on-plate alkaline degradation and SpiralTOF™ analysis (MSTips No. 284)
“Fraction base” KMD plots for a high molecular weight poly(3-hydroxybutyrate) polyester following its on-plate alkaline degradation and SpiralTOF™ analysis"(MSTips No. 283)
Visualizing fragmentation channels of polyethylene oxide with different end groups using the JMS-S3000 SpiralTOF™ with TOF–TOF option (MSTips No. 279)
“Remainders of KM” plot for polymers using msRepeatFinder: Intuitive display of High energy collision induced dissociation mass spectra acquired by SpiralTOF™/TOF (MSTips No. 270)
“Remainders of KM” plot for polymers using msRepeatFinder: compositional mapping over a broad mass range (MSTips No. 269)
Analysis of low molecular weight polyethylene with solvent-free method using JMS-S3000 “SpiralTOF™” (MSTips No. 235)
Analysis of cyanoacrylate adhesive using the JMS-S3000 “SpiralTOF™”
― Application of Kendrick Mass Defect plot analysis ― (MSTips No. 220)
Measurement of a Dendritic MS Reference Standard
Analysis of EO-PO Random Copolymer by Using a Conventional HPLC and MALDI SpiralTOF™ MS (MSTips No. 203)
Analysis of high molecular weight polystyrene standards by using JMS-S3000 SpiralTOF™ with Linear TOF option (MSTips No. 199)
Measurement of Synthetic Polymers [1]: Polystyrene (MSTips No. 163)
Measurement of Synthetic Polymers [2]: Polymethyl Methacrylate (MSTips No. 164)
Measurement of Synthetic Polymers [3]: Polyethylene Glycol (MSTips No. 165)
Detailed Structural Characterization of Polymers by MALDI-TOFMS with a Spiral Ion Trajectory (Sato, H., JEOL News, 50, 46-52, 2015)
MALDI SpiralTOF high-resolution mass spectrometry and Kendrick mass defect analysis applied to the characterization of poly(ethylene-co-vinyl acetate) copolymers (Fouquet, T., Nakamura, S. & Sato, H., Rapid Commun. Mass Spectrom. 30, 973 – 981, 2016)

Polymer Additives　P109〜

Structure analysis of a polymer additive using high-energy collision–induced dissociation mass spectra acquired by JMS-S3000 with TOF/TOF option (MSTips No. 254)
MALDI for Polymer Analysis: Synthetic Polymers and Additives (MSTips No. 205)

JMS-S3000 SpiralTOF™ series Life Science Applications Notebook 2023

Mon, 13 Oct 2025 17:55:38 GMT

Edition August 2023

Introduction and Fundamentals　P1〜

Development of JMS-S3000: MALDI-TOF/TOF Utilizing a Spiral Ion Trajectory (Takaya Satoh, JEOL News, 45, 34-37, 2010)
The Relationship between Crystal Condition and Mass Resolving Power, Mass Accuracy (MSTips No. 206)

Proteins and Peptides　P11〜

High-Resolution Measurements of Proteins Using the Spiral Mode of JMS-S3000 "SpiralTOF™" (MSTips No. 297)
MALDI-ISD Measurements Using Both the SpiralTOF™ Mode and the Linear Mode (MSTips No. 229)
High Resolution and High Mass Accuracy MALDI-ISD Measurements (MSTips No. 228)
High Sensitivity Analysis of Intact Proteins Using Linear TOF (MSTips No. 227)
High Sensitivity Peptide Measurement with the New Matrix α-Cyano-4-Chlorocinnamic Acid
Distinguishing Lysine and Glutamine in a Peptide
Analysis of Phosphopeptide Using TOF-TOF (MSTips No. 184)
Comparison of the JMS-S3000 SpiralTOF™-TOF and a 4-Sector Tandem Double-Focusing Mass Spectrometer (MSTips No. 181)
Structural Analysis of a High Molecular Weight Peptide (MSTips No. 175)
High-Energy CID Analysis of Bovine Serum Albumin (MSTips No. 174)
Analysis of Bovine Serum Albumin (MSTips No. 166)

Lipids　P39〜

Structural Analysis of Oxidized Triolein (MSTips No. 197)
Structural analysis of phospholipids in egg yolk using JMS-S3000 "SpiralTOF™" with TOF-TOF option (MSTips No. 185)
Structural Analysis of Triolein (MSTips No. 182)
Structural Analysis of Tristearin (MSTips No. 178)
Aglycon diversity of brain sterylglucosides: structure determination of cholesteryl- and sitosterylglucoside (Akiyama, H., et al., J. Lipid Res. 57, 2061–2072, 2016; open access article under the CC BY license)
Structural Analysis of Triacylglycerols by Using a MALDI-TOF/TOF System with Monoisotopic Precursor Selection (Kubo, A., et al., J. Am. Soc. Mass Spectrom., 24, 684–689, 2013; open access article ©The Authors, 2012)

Nucleic Acids / Natural Products / Oligosaccharides　P65〜

Analysis of oligonucleotides using JMS-S3000 "SpiralTOF™-plus 2.0" (MSTips No. 364)
Analysis of the Natural Organic Compound SAAF by Using the TOF-TOF Option (MSTips No. 183)
Analysis of the Natural Organic Compound YTX by Using the TOF-TOF Option
High-energy CID Mass Spectrometry of Oligosaccharides (MSTips No. 366)

Drugs / Small Molecules　P75〜

Structural analysis of a small molecule using JMS-S3000 "SpiralTOF™-plus 2.0" ー MS/MS measurement of a photodegradation product of reserpine ー (MSTips No. 365)
Analysis of a drug in urine by JMS-S3000 "SpiralTOF™" and TOF-TOF option (MSTips No. 263)
Structural analysis of melatonin and related compounds using JMS-S3000 SpiralTOF™ with the TOF-TOF option (MSTips No. 256)
Analysis of medicinal properties in a combination cold remedy by using JMS-S3000 "SpiralTOF™" (MSTips No. 241)
High-Energy CID MS-MS Analysis of Small Organic Molecules (MSTips No. 173)
MALDI for Small Molecule Analysis: A Complex Drug Mixture
Accurate mass measurement of small organic molecules using JMS-S3000 "SpiralTOF™" (MSTips No. 168)

JMS-S3000 SpiralTOF™ series Imaging Applications Notebook 2023

Mon, 13 Oct 2025 17:51:11 GMT

Edition August 2023

This is a compendium of mass spectrometry imaging applications notes and JEOL News articles based on the data acquired on JEOL ultra-high mass-resolution MALDI-TOFMS JMS-S3000 SpiralTOF™ series.

Introduction and Fundamentals　P1～

Mass Spectrometry Imaging using the JMS-S3000 "SpiralTOF™-plus" Matrix Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometer (Takaya Satoh, JEOL News, 55, 70-72, 2020)
Development of JMS-S3000: MALDI-TOF/TOF Utilizing a Spiral Ion Trajectory (Takaya Satoh, JEOL News, 45, 34-37, 2010)
High Mass Resolution MALDI-Imaging MS – High Stability of Peak Position during Imaging MS Measurement (MSTips No. 193)
The Relationship between Crystal Condition and Mass Resolving Power, Mass Accuracy (MSTips No.206)

Life Science / Biomolecules　P17〜

High mass-resolution MS imaging using JMS-S3000 "SpiralTOF™" and statistical data analysis (MSTips No. 370)
High Mass-Resolution MALDI-imaging MS for Drug Metabolism in Tissue Using the JMS-S3000 (MSTips No. 212)
High Mass Resolution MALDI-imaging MS Using JMS-S3000 SpiralTOF™ and msMicroImager (MSTips No. 211)
Fingerprint Analyses Using MALDI Imaging and SEM Imaging (MSTips No. 208)
MALDI-Imaging MS of Lipids on Mouse Brain Tissue Sections Using Negative Ion Mode (MSTips No. 196)

Polymers / Materials　P35〜

Degradation analysis of polyethylene terephthalate film by UV irradiation using imaging mass spectrometry and scanning electron microscopy (HS05)
Mass spectrometry imaging for degradation of polyethylene terephthalate by UV irradiation using JMS-S3000 "SpiralTOF™-plus" (MSTips No. 307)
A mass spectrometry imaging method for visualizing synthetic polymers combined with Kendrick mass defect analysis (MSTips No. 306)
A mass spectrometry imaging method for visualizing synthetic polymers by using average molecular weight and polydispersity as indices. (MSTips No. 305)
Mass spectrometry imaging on mixed conductive/non-conductive substrate using JMS-S3000 SpiralTOF™ (MSTips No. 288)
Analysis of organic compounds on an acrylic plate using JMS-S3000 "SpiralTOF™" (MSTips No. 251)
Ballpoint Ink Analyses Using LDI Imaging and SEM/EDS Techniques (MSTips No. 204)
Gunshot Residues (GSR) Analysis by Using MALDI Imaging
Analysis of Organic Thin Films by the Laser Desorption/Ionization Method Using the JMS-S3000 "SpiralTOF™" (Satoh, T., JEOL News, 49, 81-88, 2014)

Workflow for Molecular Structure Analysis of Transition Metal Complexes

Fri, 10 Oct 2025 15:12:54 GMT

ED2022-03E

Comprehensive analysis of transition metal complexes with XtaLAB Synergy-ED, JEOL MS and NMR

XtaLAB Synergy-ED allows molecular structure analysis of micro-crystal. This feature is quite effective in the difficult case of crystallization, such as transitional metal complexes. Furthermore, the comprehensive analysis using JEOL mass spectrometer (MS) and nuclear magnetic resonance (NMR) provides detailed information for molecular structure determination.

Structure analysis of acetylacetonate complex, Copper(II) acetylacetonate

Transition metal complexes often form complicated structures, therefore it is important to analyze them from many directions with various analytical methods for molecular structure analysis. For example, the elemental composition of a sample is analyzed with energy dispersive X-ray spectroscopy (EDS) and its molecular formula is determined by using MS (shown the left). Moreover, the molecular structure is analyzed with micro-crystals by using XtaLAB Synergy-ED (shown the center). In addition, it is effective to determine the electronic structure of a metal ion of paramagnetic complexes. NMR provides the analysis of magnetic susceptibility, and then it is possible to estimate the state of unpaired electrons (shown the right). On the other hand, for diamagnetic complexes, it is also possible to analyze the details of ligands and the state of coordination with NMR.

Elemental composition and ligands of Cu-TMEDA catalyst with SEM-EDS and NMR analysis 　

SEM-EDS provides detailed information of elemental composition, furthermore NMR allows the analysis of ligands. The chemical structures of transition metal complexes can be correctly analyzed through the comprehensive analysis of EDS and NMR. In the example below, the left-hand shows the result of the elemental composition analysis with SEM-EDS, JCM-7000 NeoScope™ and the right-hand shows the result of the ligands analysis with NMR, JNM-ECZL 500R.

Structure determination of submicron particles of Cu-TMEDA catalyst with XtaLAB Synergy-ED

XtaLAB Synergy-ED allows single crystal electron diffraction analysis with submicron particles. The result of electron diffraction analysis is refined by using the result of EDS and NMR, finally validated to show the resulting structural model.

Magnetic susceptibility of Cu-TMEDA catalyst with paramagnetic NMR spectroscopy

The presence of paramagnetic ions causes the chemical shifts of other compounds in the solution to move. This effect can be used to estimate the magnetic susceptibility of transition metal complexes and subsequently the electronic structure of transition metal ions (Evans method [1]). The results of EDS, NMR and XtaLAB Synergy-ED indicate that Cu₂₊ ion in Cu-TMEDA catalyst has a square planar structure with the Cu₂₊ ions sharing oxygen. In this structure, each Cu₂₊ ion has 3d⁹ S=1/2 for the nine d electrons of Cu₂₊ in a square‐planar field. Using the result of Evans method, the expected effective magnetic moment of electronic configuration is calculated to be 2.81 µB. As the effective magnetic moment is much closer to the value calculated from S=1 (2.83 µB), according to these results, it is thought that Cu-TMEDA catalyst shows paramagnetism of s=1 system for two copper ions.

Green: ₁H NMR spectrum of the reference solution (1-Butanol), JNM-ECZL 500R
Brown: ₁H NMR spectrum of Cu-TMEDA catalyst dissolved in the reference solution, JNM-ECZL 500R

Structural analysis of EO-PO copolymers using high-resolution MALDI-TOFMS and NMR

Fri, 10 Oct 2025 15:05:35 GMT

MSTips No.423

Ethylene oxide-propylene oxide (EO-PO) copolymer is one of copolymer type nonionic surfactants (nonionic surfactants) that combines hydrophilic EO and hydrophobic PO. It is used in various fields such as lubricants, antifoaming agents, emulsifiers, solubilizers, detergents, and antistatic agents. The physical properties of EO-PO copolymers are controlled by the sequence of EO/PO, such as random copolymerization or block copolymerization, and the number of added moles. In this application note, structural analyzes of EO-PO random copolymers and block copolymers were performed complementary by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) and solution nuclear magnetic resonance (NMR). MALDI is one of the typical soft ionization methods, and since ions derived from polymers are mainly singly charged ions, the m/z on the mass spectrum is the mass of the polymer ions. The use of high-mass-resolution MALDI-TOFMS makes it possible to easily identify polymer series due to differences in the composition of repeating units and end groups, and to calculate the molecular weight distribution of each. Using the KMD method, it became possible to easily visualize polymer series contained in complex high-mass-resolution mass spectra. From NMR, we can expect to obtain detailed structural information (random and block structures) of copolymers that are difficult to analyze by MS.

Experiment

A commercially available EO-PO random copolymer (EO-ran-PO, Mn^~2500) and a PO-EO-PO triblock copolymer (PO-EO-PO, Mn^~2700, EO 40%wt) were used as samples. In the MALDI-TOFMS experiment, DCTB was used as the matrix, and sodium trifluoroacetate (NaTFA) was used as the cationizing agent. The mass spectra were obtained using the SpiralTOF positive ion mode of JMS-S3000 “SpiralTOF™-plus2.0”. The mass spectra were deisotoped and applied KMD analysis by msRepeatFinder V6. In the NMR experiment, the ₁₃C NMR spectra of the samples dissolved in Chloroform-d were measured with JNM-ECZL 500R.

The results of MALDI-TOFMS

Figure 1 shows mass spectra and KMD plots (Base unit PO) (c) of EO-ran-PO(a) and PO-EO-PO(b). From the mass spectra, it can be seen that both have a molecular weight distribution with a molecular weight around 3000. However, the mass spectra are very complicated, and it is difficult to understand the difference in composition between the two. Therefore, the deisotoped peak lists for both are shown in a KMD plot (Base unit PO). In this plot, the distribution of PO is plotted along parallel to the horizontal axes, and the distribution of EO is plotted diagonally upward to the right. The KMD plots show that the distributions of EO and PO differ greatly between the two; EO-ran-PO has a broader EO distribution than PO, and PO-EO-PO has similar EO and PO distributions. For PO-EO-PO, homopolymers of PO oligomers were observed in the low-molecular region.

Figure 1: Mass spectra of EO-ran-PO(a), PO-EO-PO(b) and KMD plot of their deisotoped peak lists (c).

However, the KMD plot does not reveal the distribution of each degree of polymerization. Therefore, we used the search function for binary copolymers, which is a new function of msRepeatFinder V6. This function searches the peak list for binary copolymer peaks by specifying the composition of two monomers, the composition of both end groups, and the adduct ions. Here, the search was performed under the conditions shown in Table 1. A degree of polymerization (DP) plot is displayed as one of the results (Figure 2). The DP plots show the degree of polymerizations of EO (Y-axis) and PO (X-axis) and their ion intensities (area of the plots) in EO-ran-PO(a) and PO-EO-PO(b) binary copolymers. The molar ratio and weight ratio of EO and PO can also be calculated from the intensity distribution of the DP plots. The weight ratio of EO in PO-EO-PO was calculated to be 40.1%, which is in good agreement with the catalog value of 40%. A comparison of the EO/PO composition ratios obtained from ₁₃C NMR and MALDI-TOFMS will be described later.

Table 1: Search condition for the two types of measured EO-PO copolymer

Figure 2: DP plots of EO-ran-PO(a) and PO-EO-PO(b).

The results of ¹³C NMR　

Figure 3a shows the ₁₃C NMR spectrum of PO-EO-PO, which provides a lot of stereoregularity information about the chain structure of PO (Figure 3b).

Figure 3: ₁₃C NMR spectrum with proton decoupling of PO-EO-PO/ Chloroform-d(a) and the stereoregularity information on the PO linkage structure(b).

Next, Figure 4 shows the results of comparing the ₁₃C NMR spectra of PO-EO-PO and EO-ran-PO. Large differences were observed at the two points indicated by “↓” in the figure. ① is a decrease in the peak indicating the difference in the stereoregularity of consecutive POs, and ➁ is the appearance of a peak due to an increase in the number of POs adjacent to EO. These are considered to be information supporting the randomness of the EO-ran-PO sequence. Also, the peaks related to the end groups are enlarged on the right. The result suggests that only PO is present in the terminal group of PO-EO-PO, and that both EO and PO are present in EO-ran-PO, reflecting the structure of each copolymer. Finally, the EO/PO molar composition ratio determined by ₁₃C NMR quantitative measurement was compared with the results of MALDI-TOFMS, and EO-ran-PO showed good agreement. The MALDI-TOFMS results for PO-EO-PO show that the PO ratio is lower than that for ₁₃C NMR. This is a reasonable result considering that oligomers of PO are observed in the MALDI-TOFMS mass spectrum and that this amount is subtracted.

Figure 4: Comparison between ₁₃C NMR spectra with proton decoupling of PO-EO-PO/ Chloroform-d (blue), EO-ran-PO/ Chloroform-d (brown).

Table 2: The molar ratios of EO of the PO-EO-PO and EO-ran-PO calculated from NMR and MALDI-TOFMS results.

Conclusion

In this application note, two types of EO-PO copolymers were comprehensively analyzed by high-resolution MALDI-TOFMS and ₁₃C NMR.MALDI-TOFMS confirmed the molecular weight distribution of the EO-PO copolymer and enabled the calculation of the EO/PO composition ratio. This is important information because it cannot be identified by ₁₃C NMR. From the ₁₃C NMR spectrum, we were able to obtain structural information for the main chain structure, which made it possible to distinguish block and random sequences, and end group units. This is important information that cannot be obtained by MALDI-TOFMS. We also compared the EO/PO composition ratios obtained by MALDI-TOFMS and ₁₃C NMR, and found good agreement. Thus, in the analysis of polymers, it is important to combine the information obtained from MALDI-TOFMS and NMR.

Molecular Structure Analysis of Organic Compounds used in Agricultural and Food Chemistry

Fri, 10 Oct 2025 14:53:24 GMT

ED2022-06E

Structure analysis of agrochemical molecule with XtaLAB Synergy-ED, JEOL MS and NMR

Electron diffraction structure analysis of Diflubenzuron, XtaLAB Synergy-ED

Diflubenzuron is an insecticide, approved by the WHO Pesticide Evaluation Scheme. It is a widely used in forest Management and agriculture. The EPA issued a Registration Standard for diflubenzuron in September 1985 (PB86-176500). Currently, 29 diflubenzuron products are registered. XtaLAB Synergy-ED allows molecular structural analysis of submicron particles of laboratory chemicals. In addition, the comprehensive analysis by JEOL mass spectrometer (MS) and nuclear magnetic resonance (NMR) spectrometer provides details of chemical structure information for molecular structure determination.

Chemical structure analysis of Diflubenzuron

For elucidating the molecular structure of Diflubenzuron, the molecular formula is determined by using MS. In the example, the molecular formula of Diflubenzuron is provided from the Mass spectrum by using JMS-S3000 SpiralTOF™-plus 2.0 (shown top left). In addition, NMR analysis provides the details of Diflubenzuron chemical structure (shown bottom left). ₁H-₁₃C, ₁H-₁₅N and ₁H-₁₉F connectivity information is available for partial structure analysis of Diflubenzuron. ROYALPROBE™ HFX enables a wide variety of advanced ₁H and ₁₉F NMR experiments with dual tune mode. It is effective to simplify spectral assignments with ₁H with ₁₉F decoupling, ₁₉F with 1H decoupling, ₁₃C with ₁H and ₁₉F decoupling and many unique correlation experiments for the analysis of organic compounds that contain fluorine atoms, for example Diflubenzuron.

Mass spectrum of Diflubenzuron, JMS-S3000 SpiralTOF™-plus 2.0

MS/MS spectrum of Diflubenzuron

₁₃C with ₁H and ₁₉F decoupling spectrum and ₁H-₁₉F HETCOR spectrum, JNM-ECZL 500R

Chemical structure model of Diflubenzuron obtained through MS and NMR analysis

Structure refinement of Diflubenzuron

XtaLAB Synergy-ED allows single crystal analysis of Diflubenzuron with submicron particles. Diflubenzuron contains hydrogen, carbon, nitrogen, oxygen and chlorine. The molecular structure model is estimated by the electrostatic potential obtained through electron diffraction analysis. An initial structural model is refined with the result of MS and NMR analysis. The Diflubenzuron chemical structural model obtained by MS and NMR analysis provides details of the partial structure to elucidate the molecular model correctly. In the example below, the right-hand is the resulting structure of Diflubenzuron which is refined by the chemical structure model of the MS and NMR result.

Connectivity information of ₁H-₁₉F HOESY and ₁H-₁₅N HMBC correlations of Diflubenzuron, JNM-ECZL 500R

Initial structure model of Diflubenzuron, Electron diffraction structural analysis, XtaLAB Synergy-ED

Refined molecular structure of Diflubenzuron

Structure analysis of Riboflavin

Riboflavin is also known as vitamin B2. Various plants contain Riboflavin which causes no risk to human health, therefore it is excluded from pesticide residue analysis. The structure analysis workflow of Diflubenzuron is available for Riboflavin. In the following example, the workflow is applied to Riboflavin. The right-hand is the result of the refined structure of Riboflavin. This structure refinement is confirmed by the result of MS and NMR analysis. The NMR analysis provides details of the partial structure, ₁H-₁₃C and ₁H-₁₅N connectivity information to properly elucidate the molecular model.

Edited HSQC and Edited H2BC NMR spectra of Riboflavin, JNM-ECZL 500R

C-H connectivity information of Riboflavin, JNM-ECZL 500R

Refined molecular structure of Riboflavin, XtaLAB Synergy-ED

Molecular Structure Analysis of Alkaloids

Fri, 10 Oct 2025 14:22:46 GMT

ED2022-05E

Structure analysis of alkaloids with XtaLAB Synergy-ED, JEOL MS and NMR

Uncaria rhynchophylla Miquel, Structural formula of Rhynchophylline

Electron diffraction structure analysis of Rhynchophylline, XtaLAB Synergy-ED

Alkaloids are naturally occurring organic compounds that contain at least one nitrogen atom. They are mostly plant-based natural compounds and are often used as medicines. Alkaloid biosynthesis is varied; they are characterized by a great structural variety and a complicated molecular structure. XtaLAB Synergy-ED allows molecular structural analysis of submicron particles of laboratory chemicals, pharmaceutical raw materials and so on. In addition, the comprehensive analysis of the JEOL mass spectrometer (MS) and nuclear magnetic resonance (NMR) spectrometer provides details of the chemical structure information for molecular structure determination.

Chemical structure analysis of Rhynchophylline

For elucidating the molecular structure of alkaloids, the molecular formula is determined by using MS. In the example, the molecular formula of Rhynchophylline is provided from the Mass spectrum by using JMS-S3000 SpiralTOF™-plus 2.0 (shown above on the left). In addition, NMR analysis provides the details of Rhynchophylline chemical structure (shown below on the left). ¹H-₁₃C and ¹H-₁₅N connectivity information is available for partial structure analysis of Rhynchophylline. In the example below, the NMR and MS/MS spectra are analyzed complementary to estimate the total chemical structure model of Rhynchophylline.

Mass and MS/MS spectra of Rhynchophylline, JMS-S3000 SpiralTOF™-plus 2.0

Edited HSQC and Edited H2BC NMR spectra, JNM-ECZL 500R

Chemical structure model of Rhynchophylline obtained through MS and NMR analysis

Structure refinement of Rhynchophylline

XtaLAB Synergy-ED allows single crystal analysis of Rhynchophylline with submicron particles. Rhynchophylline contains hydrogen, carbon, nitrogen and oxygen. The molecular structure model is estimated by the electrostatic potential obtained through electron diffraction analysis. An initial structural model is refined with the result of MS and NMR analysis. The chemical structural model of Rhynchophylline obtained through the MS and NMR analysis provides the details of partial structure to elucidate the molecular model correctly. In the example below, the right-hand shows the resulting structure of Rhynchophylline which is refined by the chemical structure model of MS and NMR analysis.

C-H connectivity information of two-bond correlations of Rhynchophylline, Edited H2BC, JNM-ECZL 500R

Initial structure model of Rhynchophylline, Electron diffraction structure analysis, XtaLAB Synergy-ED

Refined molecular structure of Rhynchophylline

Structure analysis of another alkaloid, Cinchonidine

The structure analysis workflow of Rhynchophylline is available for additional alkaloids. In the following example, the workflow is applied to Cinchonidine. The right-hand is the result of the refined structure of Cinchonidine. This structure refinement is confirmed by the result of MS and NMR analysis. The NMR analysis provides details of the partial structure, ₁H-₁₃C and ₁H-₁₅N connectivity information, to properly dedicate the molecular model.

Edited HSQC and Edited H2BC NMR spectra of Cinchonidine, JNM-ECZL 500R

C-H connectivity information of two-bond correlations of Cinchonidine, Edited H2BC, JNM-ECZL 500R

Refined molecular structure of Cinchonidine, XtaLAB Synergy-ED

Development of peak extraction method from a high-resolution MALDI-TOF mass spectrum by machine learning focusing on peak shape, and an application to synthetic polymer analysis

Fri, 10 Oct 2025 13:39:03 GMT

MSTips No.352

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometer (MALDI-TOFMS) is a powerful tool in the analysis of polymers. High-resolution MALDI-TOFMS facilitates the identification of polymer series by the elemental composition of repeating units and end groups, and allows the calculation of the molecular weight distribution of polymers from the ionic intensity distribution. In actual industrial material analysis, mixtures of polymers with different molecular weight distributions and end groups are analyzed, and Kendrick Mass Defect (KMD) analysis, which allows an overview of complex mass spectra, is being used. KMD analysis can visualize the number and relativfe amount of polymer series contained in a complex mass spectrum because a polymer series is visualized as a straight line on a diagram called KMD plot. Another feature is that it facilitates the discovery of trace components. Since the KMD plot is created by extracting peaks from the mass spectrum, it is important to properly distinguish between the peaks to be analyzed and the noise peaks. Mass spectra of MALDI-TOFMS often show noise peaks in which the ion intensity decreases exponentially with increasing m/z. These peaks are broad, distorted in shape and poorly reproducible. In the mass spectrum measured using the high-resolution MALDI-TOFMS JMS-S3000 “SpiralTOF™” series, the peaks to be analyzed have narrow peak widths, making it possible to visually distinguish them from non-informative peaks. However, performing identification over the entire mass spectrum and including minor peaks is time consuming and inefficient. In general automatic peak determination, the peak area value is used as the ion intensity. Therefore, when a wide noise peak has the same height as a peak to be analyzed, it may be difficult to uniformly sort it out with a threshold value because the ion intensity becomes higher. Figure 1 shows the profile mass spectrum and the peaks to be analyzed and noise peaks after general peak determination. In the peak list, the peaks to be analyzed are colored red, and the noise peaks green. Weak noise peaks were observed every 1 u in the profile spectrum. In the profile spectrum, the peaks to be analyzed can be identified based on the peak width, but after the peak detection, the ion intensity (peak area) of the noise peaks becomes relatively large, making it difficult to identify the peaks to be analyzed. In order to solve this problem, this report describes the development of a method for identifying whether a peak in a mass spectrum is an analysis target peak or a noise peak using machine learning with supervised data that focuses on peak shape.

Experiment

To generate data for machine learning, polyethylene glycols (PEG) with average molecular weights of 400, 600, 1000, and 2000 were prepared at 10 mg/mL and then mixed with 1:1:2:4 (v/v/v/v) ratio (PEG mixture). In addition, a 100-fold diluted PEG mixture was prepared as a low-concentration PEG mixture.。DCTB (10 mg/mL) was used as the matrix, and sodium trifluoroacetate (1 mg/mL) was used as the cationizing agent. Mass spectra were acquired using JMS-S3000 “SpiralTOF™-plus” in SpiralTOF positive ion mode. Machine learning denoising is implemented in msPeakFinder. KMD analysis was performed with msRepeatFinder.

Figure 1 Profile mass spectrum of high-resolution MALDI-TOFMS (a) and peak list spectrum using conventional peak detection method.

Machine learning method

For machine learning, we adopted Conditional Generative Adversarial Network (cGAN). Since cGAN outputs generated data according to the input condition data, it can be considered as a conversion from condition data to generated data. This method is based on the concept of inputting the observed mass spectrum and outputting a pseudo-mass spectrum with noise peaks removed, and applied it to noise peak removal. Figure 2 shows a flow chart of the procedure for creating a machine learning model for this method. In the flow chart, the yellow background is human intervention, and the green is automatic. First, we acquired a mass spectrum of the PEG mixture for training data (Figure 3a). After the mass spectrum acquired was subjected to peak detection by a conventional method and a peak list was created, the peaks to be analyzed were determined and extracted by an expert based on the peak shape (Fig. 3b red arrow). The peaks to be analyzed were set to a constant height regardless of the observed ion intensity, and the peak shape was created with a Gaussian distribution to create a pseudo-mass spectrum (Figure 3c). In this method, the acquired mass spectrum and the pseudo mass spectrum were paired and used as the original data for the training data. Now, it takes time and effort to acquire a large number of mass spectra in order to increase the number of training data. Therefore, we created a total of 1,600 pairs of training data from one original data by dividing the original data every 1,024 points and changing the starting point of the division five times. A machine learning model was generated using the training data created in this way. Figure. 4 shows the conceptual diagram. The acquired mass spectrum is converted into a pseudo mass spectrum by the Generator. The quality of the generator was improved by discriminating the combination of this measured mass spectrum and the pseudo mass spectrum converted through the generator, and the combination of the measured mass spectrum and the pseudo mass spectrum of the training data, with the discriminator.

Figure 2 Flowchart of making the machine learning model.

Figure 3 The relationship between profile mass spectrum(a), peak list(b) and pseudo-mass spectrum(c).

Figure 4 The scheme of making the machine learning model using cGAN.

Validation and application of the machine learning model

Next, the procedure for actual noise removal using the generated machine learning model is shown (Figure 5). In the flow chart, the yellow background is human intervention, and the green is automatic. The acquired mass spectrum is subjected to peak detection by the conventional method, and in parallel with this, it is divided into 1,024 points and converted into pseudo mass spectra using the machine learning model of the peaks determined by the conventional method, only peaks that match the peak positions of the pseudo mass spectrum are left, and a noise-removed peak list is generated. In other words, the m/z and ion intensity of the peak list extracted by this method are those of the conventional method. Here, we tried to remove noise peaks from the PEG mixture mass spectrum, which was used to create training data. The results are summarized in Table 1. A total of 4,390 peaks were detected from the mass spectrum of the PEG mixture by the conventional method. Among them, 1,265 peaks in the upper left and 3,105 peaks in the lower right (99.5% of the total) match the results of the judgments made by the machine learning model with the judgments made manually when the training data was created. The 14 peaks on the upper right were determined as peaks to be analyzed when the machine learning model was created, but were determined as noise peaks by the machine learning model. It was confirmed that these peak shapes were slightly distorted and difficult to judge even by an expert. The six peaks on the lower left were determined as noise peaks when the training data was created, but were determined as peaks to be analyzed by the machine learning model. It was confirmed that these were caused by human error when preparing the training data. After that, machine learning was performed again with the training data that corrected this mistake. We believe that it is effective to validate the model by using the mass spectrum that was used to create the machine learning model. Finally, peak extraction was performed using the mass spectrum of low-concentration PEG, and the results developed into a KMD plot are shown in Figure 6. Figure 6a is the measured mass spectrum and Figure 6b is the KMD plot. The red points in the KMD plot were determined as peaks to be analyzed by machine learning, and the green points were determined as noise peaks. From this result, it can be seen that the PEG series are well visualized by removing noise, especially in the region of m/z < 1,500.

Summary

As described above, we were able to show that the KMD analysis can be performed more efficiently by removing the noise peaks that are often observed in the low-m/z region from the high-resolution MALDI-TOFMS data using a machine learning model.

Figure 5 Flowchart of making the extract peak list by the machine learning model.

Table 1 Comparison between the peak lists of PEG mixture used as the training data and the one extracted by the machine learning model.

Figure 6 Mass spectrum of low concentration PEG mixture(a) and the KMD plot of the extracted peak list (red) and the noise peal list (green) separated by the machine learning model.

Comprehensive Analysis of Acetylacetonate Complexes

Fri, 10 Oct 2025 13:13:47 GMT

ED2022-04E

Various structural analysis methods for acetylacetonate complexes with XtaLAB Synergy-ED, JEOL MS and NMR

XtaLAB Synergy-ED allows molecular structural analysis of micro-crystals. It is quite effective for transition metal complexes in the difficult case of crystallization. Acetylacetonate (acac) anion is a bidentate ligand and shows various complex compounds. The ligand is an ion that bonds to a central transition metal to form octahedral, tetrahedral, square planar and so on. In the example below, results are shown of electron diffraction structural analysis of Chromium(III) acetylacetonate (Cr(acac)₃), Vanadyl (II) acetylacetonate (Vo(acac)₂) and Copper(II) acetylacetonate (Cu(acac)₂) complexes by XtaLAB Synergy-ED and the schematic diagram of 3d orbital.

Electron diffraction structure analysis of Cr(acac)₃, Vo(acac)₂ and Cu(acac)₂ complexes, XtaLAB Synergy-ED

Structure analysis of octahedral acetylacetonate complex, Rhodium(III) acetylacetonate 　

JEOL mass spectrometer (MS) and nuclear magnetic resonance (NMR) spectrometer provide details of chemical structure information of acetylacetonate complexes for molecular structure determination. In the example below, the left-hand is the result of MS and NMR analysis and the right-hand shows the resulting structural model from electron diffraction structure analysis of Rh(acac)₃.

Above: Mass spectrum of Rh(acac)₃, JMS-S3000 SpiralTOF™-plus 2.0
Below: ₁H NMR spectrum of Rh(acac)₃, JNM-ECZL 500R

Electron diffraction structure analysis of Rh(acac)₃, XtaLAB Synergy-ED

Spin state analysis of octahedral acetylacetonate complex

Rh(acac)₃ has two possible electron configurations, denoted as high spin of 4d₆ S=2 and low spin of 4d₆ S=0. On the other hands, Cr(acac)₃ has a spin state of d₃ S=3/2 and shows paramagnetism. In the example below, the green line is a ₁H NMR spectrum of Cr(acac)₃ and the brown line is Rh(acac)₃. The peaks of ligand of Cr(acac)₃ spectrum are sifted and broadened with the Fermi-contact interaction of Cr₃₊ and the ligand. On the other hand, the spectrum of Rh(acac)₃ shows that the peaks of the ligand are not sifted and its electron configuration is the low spin state 4d₆ S=0.

Green: ₁H NMR spectrum of Cr(acac)₃, JNM-ECZL 500R
Brown: ₁H NMR spectrum of Rh(acac)₃, JNM-ECZL 500R

Magnetic susceptibility and electron configuration of acetylacetonate complexes

The electron configuration of the transition metal is an important factor in the molecular structure of transition metal complexes. The unpaired electrons of the transition metal can be estimated by determining magnetic sensitivity. The magnetic susceptibility of complexes comes from the magnetism at the atomic level of which they are made, and is dominated by the magnetic moments of electrons. The presence of paramagnetic ions causes the chemical shifts of other compounds in the solution to move. This effect can be used to estimate the magnetic susceptibility of transition metal complexes and subsequently the electronic structure of transition metal ions (Evans method [1]). With the result of the Evans method, the effective magnetic moment is calculated to determine the electron structure of transition metal. In the example below, the table shows the effective magnetic moment of Cr(acac)₃, VO(acac)₂ and Cu(acac)₂ complexes estimated using ₁H NMR spectra.

[1] D. F. Evans, J. Chem. Soc. 1959, 2003.
[2] C. Kittel, Introduction to Solid State Physics, 7th edition

JEOL Resources

Polymeric Materials Analysis by JEOL Mass Spectrometers - A Guidebook

JMS-S3000 SpiralTOF™ series Polymeric Materials Applications Notebook 2024

Table of Contents

Introduction and Fundamentals P1〜

Polymers / Oligomers P15〜

Polymer Additives P109〜

JMS-S3000 SpiralTOF™ series Life Science Applications Notebook 2023

Table of Contents

Introduction and Fundamentals P1〜

Proteins and Peptides P11〜

Lipids P39〜

Nucleic Acids / Natural Products / Oligosaccharides P65〜

Drugs / Small Molecules P75〜

JMS-S3000 SpiralTOF™ series Imaging Applications Notebook 2023

Table of Contents

Introduction and Fundamentals P1～

Life Science / Biomolecules P17〜

Polymers / Materials P35〜

Workflow for Molecular Structure Analysis of Transition Metal Complexes

Comprehensive analysis of transition metal complexes with XtaLAB Synergy-ED, JEOL MS and NMR

Structure analysis of acetylacetonate complex, Copper(II) acetylacetonate

Elemental composition and ligands of Cu-TMEDA catalyst with SEM-EDS and NMR analysis

Structure determination of submicron particles of Cu-TMEDA catalyst with XtaLAB Synergy-ED

Magnetic susceptibility of Cu-TMEDA catalyst with paramagnetic NMR spectroscopy

Structural analysis of EO-PO copolymers using high-resolution MALDI-TOFMS and NMR

Experiment

The results of MALDI-TOFMS

The results of 13C NMR

Conclusion

Molecular Structure Analysis of Organic Compounds used in Agricultural and Food Chemistry

Structure analysis of agrochemical molecule with XtaLAB Synergy-ED, JEOL MS and NMR

Chemical structure analysis of Diflubenzuron

Structure refinement of Diflubenzuron

Structure analysis of Riboflavin

Molecular Structure Analysis of Alkaloids

Structure analysis of alkaloids with XtaLAB Synergy-ED, JEOL MS and NMR

Chemical structure analysis of Rhynchophylline

Structure refinement of Rhynchophylline

Structure analysis of another alkaloid, Cinchonidine

Development of peak extraction method from a high-resolution MALDI-TOF mass spectrum by machine learning focusing on peak shape, and an application to synthetic polymer analysis

Experiment

Machine learning method

Validation and application of the machine learning model

Summary

Comprehensive Analysis of Acetylacetonate Complexes

Various structural analysis methods for acetylacetonate complexes with XtaLAB Synergy-ED, JEOL MS and NMR

Structure analysis of octahedral acetylacetonate complex, Rhodium(III) acetylacetonate

Spin state analysis of octahedral acetylacetonate complex

Magnetic susceptibility and electron configuration of acetylacetonate complexes

Introduction and Fundamentals　P1〜

Polymers / Oligomers　P15〜

Polymer Additives　P109〜

Introduction and Fundamentals　P1〜

Proteins and Peptides　P11〜

Lipids　P39〜

Nucleic Acids / Natural Products / Oligosaccharides　P65〜

Drugs / Small Molecules　P75〜

Introduction and Fundamentals　P1～

Life Science / Biomolecules　P17〜

Polymers / Materials　P35〜

Elemental composition and ligands of Cu-TMEDA catalyst with SEM-EDS and NMR analysis 　

The results of ¹³C NMR　

Structure analysis of octahedral acetylacetonate complex, Rhodium(III) acetylacetonate 　