CONTINfit-GRNN: a hybrid method for recovering nanoparticle size distribution in dynamic light scattering
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Numerical methods in inverse problems · Electrical and Bioimpedance Tomography · Photoacoustic and Ultrasonic Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (37)
Show source-provided references
1. Bowen P (2002) Particle size distribution measurement from millimeters to nanometers and from rods to platelets. J Dispers Sci Technol 23(5):631–662. https://doi.org/10.1081/DIS-12001536810.1081/DIS-120015368 ↗
2. Mitchell MJ, Billingsley MM, Haley RM et al (2021) Engineering precision nanoparticles for drug delivery. Nat Rev Drug Discov 20(2):101–12410.1038/s41573-020-0090-8 ↗
3. Filippov SK, Khusnutdinov R, Murmiliuk A et al (2023) Dynamic light scattering and transmission electron microscopy in drug delivery: a roadmap for correct characterization of nanoparticles and interpretation of results. Mater Horiz 10(12):5354–5370. https://doi.org/10.1039/D3MH00717K10.1039/D3MH00717K ↗
4. Astruc D (2020) Introduction: nanoparticles in catalysis. Chem Rev 120(2):461–463. https://doi.org/10.1021/acs.chemrev.8b0069610.1021/acs.chemrev.8b00696 ↗
5. Wang L, Teles MPR, Arabkoohsar A et al (2022) A holistic and state-of-the-art review of nanotechnology in solar cells. Sustain Energy Technol Assess 54:102864. https://doi.org/10.1016/j.seta.2022.10286410.1016/j.seta.2022.102864 ↗
6. Carney RP, Kim JY, Qian H et al (2011) Determination of nanoparticle size distribution together with density or molecular weight by 2D analytical ultracentrifugation. Nat Commun 2(1):33510.1038/ncomms1338 ↗
7. Filipe V, Hawe A, Jiskoot W (2010) Critical evaluation of nanoparticle tracking analysis (NTA) by NanoSight for the measurement of nanoparticles and protein aggregates. Pharm Res 27(5):796–810. https://doi.org/10.1007/s11095-010-0073-210.1007/s11095-010-0073-2 ↗
8. Wilson BK, Prud’homme RK (2021) Nanoparticle size distribution quantification from transmission electron microscopy (TEM) of ruthenium tetroxide stained polymeric nanoparticles. J Colloid Interface Sci 604:208–220. https://doi.org/10.1016/j.jcis.2021.04.08110.1016/j.jcis.2021.04.081 ↗
9. Berne BJ, Pecora R (2020) Dynamic Light Scattering: With Applications to Chemistry, Biology, and Physics. Dover
10. Pecora R (2000) Dynamic light scattering measurement of nanometer particles in liquids. J Nanopart Res 2(2):123–131. https://doi.org/10.1023/A:101006710718210.1023/A:1010067107182 ↗
11. Kaszuba M, McKnight D, Connah MT et al (2008) Measuring sub nanometre sizes using dynamic light scattering. J Nanopart Res 10(5):823–829. https://doi.org/10.1007/s11051-007-9317-410.1007/s11051-007-9317-4 ↗
12. Zhu X, Shen J, Liu W et al (2010) Nonnegative least-squares truncated singular value decomposition to particle size distribution inversion from dynamic light scattering data. Appl Opt 49(34):6591–659610.1364/AO.49.006591 ↗
13. Kabanikhin SI (2008) Definitions and examples of inverse and ill-posed problems. J Inverse Ill-Posed Probl 16(4):317–357. https://doi.org/10.1515/JIIP.2008.01910.1515/JIIP.2008.019 ↗
14. Lavrentʹev MM, Romanov VG, Shishatskiĭ SP (1986) Ill-posed Problems of Mathematical Physics and Analysis. American Mathematical Society10.1090/mmono/064 ↗
15. Koppel DE (1972) Analysis of macromolecular polydispersity in intensity correlation spectroscopy: the method of cumulants. J Chem Phys 57(11):4814–4820. https://doi.org/10.1063/1.167815310.1063/1.1678153 ↗
16. Mailer AG, Clegg PS, Pusey PN (2015) Particle sizing by dynamic light scattering: non-linear cumulant analysis. J Phys Condens Matter 27(14):145102. https://doi.org/10.1088/0953-8984/27/14/14510210.1088/0953-8984/27/14/145102 ↗
17. Lawson CL, Hanson RJ (1995) Solving least squares problems. Soc for Industrial & Applied Math10.1137/1.9781611971217 ↗
18. Guo X, Chen M, Peng L et al (2022) Particle size distribution inversion in dynamic light scattering by adaptive step-size non-negative least squares. Opt Commun 503:127444. https://doi.org/10.1016/j.optcom.2021.12744410.1016/j.optcom.2021.127444 ↗
19. Wang YJ, Shen J, Liu W et al (2013) Non-negative constraint research of Tikhonov regularization inversion for dynamic light scattering. Laser Phys 23(8):085701. https://doi.org/10.1088/1054-660X/23/8/08570110.1088/1054-660X/23/8/085701 ↗
20. Landi G, Zama F (2006) The active-set method for nonnegative regularization of linear ill-posed problems. Appl Math Comput 175(1):715–729. https://doi.org/10.1016/j.amc.2005.07.03710.1016/j.amc.2005.07.037 ↗
21. Provencher SW (1982) A constrained regularization method for inverting data represented by linear algebraic or integral equations. Comput Phys Commun 27(3):213–227. https://doi.org/10.1016/0010-4655(82)90173-410.1016/0010-4655(82)90173-4 ↗
22. Scotti A, Liu W, Hyatt JS et al (2015) The CONTIN algorithm and its application to determine the size distribution of microgel suspensions. J Chem Phys 142(23):234905. https://doi.org/10.1063/1.492168610.1063/1.4921686 ↗
23. Ruf H, Gould BJ, Haase W (2000) The effect of nonrandom errors on the results from regularized inversions of dynamic light scattering data. Langmuir 16(2):471–480. https://doi.org/10.1021/la990630i10.1021/la990630i ↗
24. Li C, Dou Z, Wang YJ et al (2024) Particle size inversion based on L1,∞-constrained regularization model in dynamic light scattering. Photonics 11:1041. https://doi.org/10.3390/ma1520711110.3390/ma15207111 ↗
25. Qi T, Liu W, Thomas JC et al (2022) Determination of optimal autocorrelation function truncation point for particle size measurement with dynamic light scattering. Acta Photonica Sinica 51(11):329001
26. Wang S, Tao Y, Dong X et al (2005) Analysis and comparison of arithmetic for inverting particle size distribution from photon correlation spectrum. China Powder Sci Technol 11(1):27–32
27. Ren Y, Mao J, Zhao H et al (2020) Prediction of aerosol particle size distribution based on neural network. Adv Meteorol 2020(1):5074192. https://doi.org/10.1155/2020/507419210.1155/2020/5074192 ↗
28. Chicea D (2017) Using neural networks for dynamic light scattering time series processing. Meas Sci Technol 28(5):055206. https://doi.org/10.1088/1361-6501/aa61b410.1088/1361-6501/aa61b4 ↗
29. Farzi GA, Nejad AP (2016) An image-based technique for measuring droplet size distribution: the use of CNN algorithm. J Dispers Sci Technol 37(10):1444–1452. https://doi.org/10.1080/01932691.2015.109032110.1080/01932691.2015.1090321 ↗
30. Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2(6):568–576. https://doi.org/10.1109/72.9793410.1109/72.97934 ↗
31. Gugliotta LM, Stegmayer GS, Clementi LA et al (2009) A neural network model for estimating the particle size distribution of dilute latex from multiangle dynamic light scattering measurements. Part Part Syst Charact 26(1–2):41–52. https://doi.org/10.1002/ppsc.20080001010.1002/ppsc.200800010 ↗
32. Wang Z, Wang Y, Shen J et al (2022) Dynamic light scattering inversion based on Tikhonov regularization and parameter-optimized generalized regression neural network. Powder Technol 409:117802. https://doi.org/10.1016/j.powtec.2022.11780210.1016/j.powtec.2022.117802 ↗
33. Xu J, Xia M, Yang K et al (2023) Machine learning inversion algorithm for particle size distribution of non-spherical particle systems. Acta Optica Sinica 43(9):258–268
34. Bohren CF, Huffman DR (1998) Absorption and Scattering of Light by Small Particles. John Wiley & Sons10.1002/9783527618156 ↗
35. Hansen PC, O’Leary DP (1993) The use of the L-curve in the regularization of discrete ill-posed problems. SIAM J Sci Comput 14(6):1487–1503. https://doi.org/10.1137/091408610.1137/0914086 ↗
36. Chen M, Su H, Zhou Y et al (2016) Automatic selection of regularization parameters for dynamic fluorescence molecular tomography: a comparison of L-curve and U-curve methods. Biomed Opt Express 7(12):5021–5041. https://doi.org/10.1364/BOE.7.00502110.1364/BOE.7.005021 ↗
37. Yu L, Yang G, He Z et al (2007) Analysis on the constrained regularization inversing CONTIN algorithm in particle sizing. Acta Laser Biology Sinica 16(1):74–78