Rapid identification of pathogens with higher sensitivity and specificity plays a significant role in maintaining public health, environmental monitoring, controlling food quality, and clinical diagnostics. Different methods have been widely used in food testing laboratories, quality control departments in food companies, hospitals, and clinical settings to identify pathogens. Some limitations in current pathogens detection methods are time-consuming, expensive, and laborious sample preparation, making it unsuitable for rapid detection. Microfluidics has emerged as a promising technology for biosensing applications due to its ability to precisely manipulate small volumes of fluids. Microfluidics platforms combined with spectroscopic techniques are capable of developing miniaturized devices that can detect and quantify pathogenic samples. The review focuses on the advancements in microfluidic devices integrated with spectroscopic methods for detecting bacterial microbes over the past five years. The review is based on several spectroscopic techniques, including fluorescence detection, surface-enhanced Raman scattering, and dynamic light scattering methods coupled with microfluidic platforms. The key detection principles of different approaches were discussed and summarized. Finally, the future possible directions and challenges in microfluidic-based spectroscopy for isolating and detecting pathogens using the latest innovations were also discussed.

1.
S. M.
Pires
et al, “
Burden of foodborne diseases: Think global, act local
,”
Curr. Opin. Food Sci.
39
,
152
159
(
2021
).
2.
V.
Escobar
et al, “
Recent advances on peptide-based biosensors and electronic noses for foodborne pathogen detection
,”
Biosensors
13
,
258
(
2023
).
3.
D.
Gao
,
Z.
Ma
, and
Y.
Jiang
, “
Recent advances in microfluidic devices for foodborne pathogens detection
,”
TrAC, Trends Anal. Chem.
157
,
116788
(
2022
).
4.
E.
Todd
, “
Food-Borne disease prevention and risk assessment
,”
Int. J. Environ. Res. Public Health
17
,
5129
(
2020
).
5.
J.
Das
and
H. N.
Mishra
, “
Recent advances in sensors for detecting food pathogens, contaminants, and toxins: A review
,”
Eur. Food Res. Technol.
248
(
4
),
1125
1148
(
2022
).
6.
P.
Rajapaksha
et al, “
A review of methods for the detection of pathogenic microorganisms
,”
Analyst
144
(
2
),
396
411
(
2019
).
7.
R. A.
Wu
et al, “
Recent advances in understanding the effect of acid-adaptation on the cross-protection to food-related stress of common foodborne pathogens
,”
Crit. Rev. Food Sci. Nutr.
62
(
26
),
7336
7353
(
2022
).
8.
O.
Alegbeleye
and
A. S.
Sant’Ana
, “
Survival and growth behaviour of listeria monocytogenes in ready-to-eat vegetable salads
,”
Food Control
138
,
109023
(
2022
).
9.
M.
Ferone
et al, “
Microbial detection and identification methods: Bench top assays to omics approaches
,”
Compr. Rev. Food Sci. Food Saf.
19
(
6
),
3106
3129
(
2020
).
10.
Y.
Chen
et al, “
Nucleic acid amplification free biosensors for pathogen detection
,”
Biosens. Bioelectron.
153
,
112049
(
2020
).
11.
S.
MacAulay
et al, “
Moving towards improved surveillance and earlier diagnosis of aquatic pathogens: From traditional methods to emerging technologies
,”
Rev. Aquacult.
14
(
4
),
1813
1829
(
2022
).
12.
A.
Saravanan
et al, “
Methods of detection of food-borne pathogens: A review
,”
Environ. Chem. Lett.
19
(
1
),
189
207
(
2021
).
13.
Q.
Ali
et al, “
Advances, limitations, and prospects of biosensing technology for detecting phytopathogenic bacteria
,”
Chemosphere
296
,
133773
(
2022
).
14.
H. R.
Safford
and
H. N.
Bischel
, “
Flow cytometry applications in water treatment, distribution, and reuse: A review
,”
Water Res.
151
,
110
133
(
2019
).
15.
R.
Zhang
et al, “
Nanomaterial-based biosensors for sensing key foodborne pathogens: Advances from recent decades
,”
Compr. Rev. Food Sci. Food Saf.
19
(
4
),
1465
1487
(
2020
).
16.
N.
Garg
,
F. J.
Ahmad
, and
S.
Kar
, “
Recent advances in loop-mediated isothermal amplification (LAMP) for rapid and efficient detection of pathogens
,”
Curr. Res. Microb. Sci.
3
,
100120
(
2022
).
17.
D.
Das
,
C.-W.
Lin
, and
H.-S.
Chuang
, “
LAMP-based point-of-care biosensors for rapid pathogen detection
,”
Biosensors
12
,
1068
(
2022
).
18.
X.
Zhang
et al, “
Evolution of the probe-based loop-mediated isothermal amplification (LAMP) assays in pathogen detection
,”
Diagnostics
13
,
1530
(
2023
).
19.
N.
Li
et al, “
A specific mass-tag approach for detection of foodborne pathogens using MALDI-TOF mass spectrometry
,”
Anal. Chem.
94
(
9
),
3963
3969
(
2022
).
20.
X.-F.
Chen
et al, “
Matrix-Assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) analysis for the identification of pathogenic microorganisms: A review
,”
Microorganisms
9
,
1536
(
2021
).
21.
A.
Haider
et al, “
The current level of MALDI-TOF MS applications in the detection of microorganisms: A short review of benefits and limitations
,”
Microbiol. Res.
14
,
80
90
(
2023
).
22.
I.
Sivanesan
et al, “
A systematic assessment of matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) application for rapid identification of pathogenic microbes that affect food crops: Delivered and future deliverables
,”
RSC Adv.
13
(
25
),
17297
17314
(
2023
).
23.
X.
Lv
et al, “
Multicolor and ultrasensitive enzyme-linked immunosorbent assay based on the fluorescence hybrid chain reaction for simultaneous detection of pathogens
,”
J. Agric. Food Chem.
67
(
33
),
9390
9398
(
2019
).
24.
R.
Ahirwar
,
A.
Bhattacharya
, and
S.
Kumar
, “
Unveiling the underpinnings of various non-conventional ELISA variants: A review article
,”
Expert Rev. Mol. Diagn.
22
(
7
),
761
774
(
2022
).
25.
L. M.
Castle
et al, “
Electrochemical sensors to detect bacterial foodborne pathogens
,”
ACS Sens.
6
(
5
),
1717
1730
(
2021
).
26.
S.
Panwar
et al, “
Advanced diagnostic methods for identification of bacterial foodborne pathogens: Contemporary and upcoming challenges
,”
Crit. Rev. Biotechnol.
43
(
7
),
982
1000
(
2023
).
27.
S.
Pandian
et al, “
Spectroscopic methods for the detection of microbial pathogens and diagnostics of infectious diseases—An updated overview
,”
Processes
11
,
1191
(
2023
).
28.
A.
Jagannath
et al, “
Pathogen detection on microfluidic platforms: Recent advances, challenges, and prospects
,”
Biosens. Bioelectron. X
10
,
100134
(
2022
).
29.
M.
Usman
et al, “
Recent advances in surface enhanced Raman spectroscopy for bacterial pathogen identifications
,”
J. Adv. Res.
51
,
91
107
(
2023
).
30.
X.
Liu
et al, “
Improvement of the signal to noise ratio for fluorescent imaging in microfluidic chips
,”
Sci. Rep.
12
(
1
),
18911
(
2022
).
31.
V.
Vaishampayan
,
A.
Kapoor
, and
S. P.
Gumfekar
, “
Enhancement in the limit of detection of lab-on-chip microfluidic devices using functional nanomaterials
,”
Can. J. Chem. Eng.
101
(
9
),
5208
5221
(
2023
).
32.
B.-L.
Li
et al, “
Hollow core micro-fiber for optical wave guiding and microfluidic manipulation
,”
Sens. Actuators B
262
,
953
957
(
2018
).
33.
S.
Täuber
,
E.
von Lieres
, and
A.
Grünberger
, “
Dynamic environmental control in microfluidic single-cell cultivations: From concepts to applications
,”
Small
16
(
16
),
1906670
(
2020
).
34.
X.
Fu
et al, “
Application progress of microfluidics-integrated biosensing platforms in the detection of foodborne pathogens
,”
Trends Food Sci. Technol.
116
,
115
129
(
2021
).
35.
F.
Mi
et al, “
Recent advancements in microfluidic chip biosensor detection of foodborne pathogenic bacteria: A review
,”
Anal. Bioanal. Chem.
414
(
9
),
2883
2902
(
2022
).
36.
A.
Locke
,
S.
Fitzgerald
, and
A.
Mahadevan-Jansen
, “
Advances in optical detection of human-associated pathogenic bacteria
,”
Molecules
25
,
5256
(
2020
).
37.
M. J.
Pioz
et al, “
A review of optical point-of-care devices to estimate the technology transfer of these cutting-edge technologies
,”
Biosensors
12
,
1091
(
2022
).
38.
W.
Li
et al, “
Review of paper-based microfluidic analytical devices for in-field testing of pathogens
,”
Anal. Chim. Acta
1278
,
341614
(
2023
).
39.
S.
Battat
,
D. A.
Weitz
, and
G. M.
Whitesides
, “
An outlook on microfluidics: The promise and the challenge
,”
Lab Chip
22
(
3
),
530
536
(
2022
).
40.
X.
Han
et al, “
Polymer-based microfluidic devices: A comprehensive review on preparation and applications
,”
Polym. Eng. Sci.
62
(
1
),
3
24
(
2022
).
41.
C. R.
Nemr
et al, “
Digital microfluidics as an emerging tool for bacterial protocols
,”
SLAS Technol.
28
(
1
),
2
15
(
2023
).
42.
X.
Liu
et al, “
Single-cell HER2 quantification via instant signal amplification in microdroplets
,”
Anal. Chim. Acta
1251
,
340976
(
2023
).
43.
R.
Ning
et al, “
Recent developments of droplets-based microfluidics for bacterial analysis
,”
Chin. Chem. Lett.
33
(
5
),
2243
2252
(
2022
).
44.
W.
Lei
et al, “
Droplet microarray as a powerful platform for seeking new antibiotics against multidrug-resistant bacteria
,”
Adv. Biol.
6
(
12
),
2200166
(
2022
).
45.
P.
Nath
,
K. R.
Mahtaba
, and
A.
Ray
, “
Fluorescence-based portable assays for detection of biological and chemical analytes
,”
Sensors
23
,
5053
(
2023
).
46.
C.
Spatola Rossi
et al, “
Microfluidics for rapid detection of live pathogens
,”
Adv. Funct. Mater.
33
(
21
),
2212081
(
2023
).
47.
X.
Liao
et al, “
Advancing point-of-care microbial pathogens detection by material-functionalized microfluidic systems
,”
Trends Food Sci. Technol.
135
,
115
130
(
2023
).
48.
I.
Bobrinetskiy
et al, “
Advances in nanomaterials-based electrochemical biosensors for foodborne pathogen detection
,”
Nanomaterials
11
,
2700
(
2021
).
49.
J.
Qin
et al, “
Emerging biosensing and transducing techniques for potential applications in point-of-care diagnostics
,”
Chem. Sci.
13
(
10
),
2857
2876
(
2022
).
50.
S.
Kakkar
et al, “
Progress in fluorescence biosensing and food safety towards point-of-detection (PoD) system
,”
Biosensors
13
,
249
(
2023
).
51.
Y.
Shang
et al, “
Advances in nanomaterial-based microfluidic platforms for on-site detection of foodborne bacteria
,”
TrAC, Trends Anal. Chem.
147
,
116509
(
2022
).
52.
Z.
Li
et al, “
Fluorescent sensor array based on aggregation-induced emission luminogens for pathogen discrimination
,”
Analyst
147
(
13
),
2930
2935
(
2022
).
53.
S.
Kaushal
et al, “
Culture-Free quantification of bacteria using digital fluorescence imaging in a tunable magnetic capturing cartridge for onsite food testing
,”
ACS Sens.
7
(
8
),
2188
2197
(
2022
).
54.
M.
Luo
,
H.
Yukawa
, and
Y.
Baba
, “
Micro-/nano-fluidic devices and in vivo fluorescence imaging based on quantum dots for cytologic diagnosis
,”
Lab Chip
22
(
12
),
2223
2236
(
2022
).
55.
Y.
Ding
et al, “
Artificial intelligence-assisted point-of-care testing system for ultrafast and quantitative detection of drug-resistant bacteria
,”
SmartMat
2023
,
e1214
.
56.
R. D.
McClelland
,
T. N.
Culp
, and
D. J.
Marchant
, “
Imaging flow cytometry and confocal immunofluorescence microscopy of virus-host cell interactions
,”
Front. Cell. Infect. Microbiol.
11
,
749039
(
2021
).
57.
K.
Wu
et al, “
Recent progress of microfluidic chips in immunoassay
,”
Front. Bioeng. Biotechnol.
10
,
1112327
(
2022
).
58.
C.-H.
Wang
et al, “
Rapid molecular diagnosis of live mycobacterium tuberculosis on an integrated microfluidic system
,”
Sens. Actuators, B
365
,
131968
(
2022
).
59.
T.
Li
et al, “
Naked-eye based point-of-care detection of E.coli O157: H7 by a signal-amplified microfluidic aptasensor
,”
Anal. Chim. Acta
1130
,
20
28
(
2020
).
60.
L. F.
Alonzo
et al, “
A microfluidic device and instrument prototypes for the detection of Escherichia coli in water samples using a phage-based bioluminescence assay
,”
Lab Chip
22
(
11
),
2155
2164
(
2022
).
61.
A. M.
Kaushik
et al, “
Accelerating bacterial growth detection and antimicrobial susceptibility assessment in integrated picoliter droplet platform
,”
Biosens. Bioelectron.
97
,
260
266
(
2017
).
62.
S.
Rauf
et al, “
Digital E. coli counter: A microfluidics and computer vision-based DNAzyme method for the isolation and specific detection of E. coli from water samples
,”
Biosensors
12
,
34
(
2022
).
63.
J. S.
Shah
and
R.
Ramasamy
, “
Fluorescence in situ hybridization (FISH) tests for identifying protozoan and bacterial pathogens in infectious diseases
,”
Diagnostics
12
,
1286
(
2022
).
64.
J.
Gu
et al, “
Application of fluorescence in situ hybridization (FISH) in oral microbial detection
,”
Pathogens
11
,
1450
(
2022
).
65.
P.
Rodriguez-Mateos
et al, “
FISH and chips: A review of microfluidic platforms for FISH analysis
,”
Med. Microbiol. Immunol.
209
(
3
),
373
391
(
2020
).
66.
V. B.
Barbosa
et al, “
Microfluidics combined with fluorescence in situ hybridization (FISH) for candida spp.: Detection
,”
Front. Bioeng. Biotechnol.
10
,
987669
(
2022
).
67.
M. S.
Lee
et al, “
Quantitative fluorescence in situ hybridization (FISH) of magnetically confined bacteria enables early detection of human bacteremia
,”
Small Methods
6
(
3
),
2101239
(
2022
).
68.
N.
Yamaguchi
and
S.
Goto
, “
Rapid quantification of Escherichia coli in potable water by fluorescence in situ hybridization performed in liquid (liq-FISH) and a microfluidic system
,”
Water Air Soil Pollut.
230
(
12
),
285
(
2019
).
69.
Y.-T.
Kao
et al, “
Microfluidic one-pot digital droplet FISH using LNA/DNA molecular beacons for bacteria detection and absolute quantification
,”
Biosensors
12
,
237
(
2022
).
70.
T.
Xu
et al, “
A milliliter to picoliter-level centrifugal microfluidic concentrator for fast pathogen detection and antimicrobial susceptibility testing
,”
Sens. Actuators B
343
,
130117
(
2021
).
71.
H.-T.
Li
et al, “
A minimalist approach for generating picoliter to nanoliter droplets based on an asymmetrical beveled capillary and its application in digital PCR assay
,”
Talanta
217
,
120997
(
2020
).
72.
A.
Ruszczak
et al, “
Droplet-based methods for tackling antimicrobial resistance
,”
Curr. Opin. Biotechnol.
76
,
102755
(
2022
).
73.
D.
Cai
et al, “
Droplet encoding-pairing enabled multiplexed digital loop-mediated isothermal amplification for simultaneous quantitative detection of multiple pathogens
,”
Adv. Sci.
10
(
7
),
2205863
(
2023
).
74.
H.
Yuan
et al, “
Hand-powered microfluidics for parallel droplet digital loop-mediated isothermal amplification assays
,”
ACS Sens.
6
(
8
),
2868
2874
(
2021
).
75.
C.
Shao
et al, “
Droplet microfluidics-based biomedical microcarriers
,”
Acta Biomater.
138
,
21
33
(
2022
).
76.
H.
Feng
et al, “
Droplet-based microfluidics systems in biomedical applications
,”
Electrophoresis
40
(
11
),
1580
1590
(
2019
).
77.
A.
Ge
et al, “
Label-free droplet-based bacterial growth phenotype screening by a mini integrated microfluidic platform
,”
Sens. Actuators B
385
,
133691
(
2023
).
78.
É.
Geersens
,
S.
Vuilleumier
, and
M.
Ryckelynck
, “
Growth-associated droplet shrinkage for bacterial quantification, growth monitoring, and separation by ultrahigh-throughput microfluidics
,”
ACS Omega
7
(
14
),
12039
12047
(
2022
).
79.
T. N.
Trinh
and
N. Y.
Lee
, “
Advances in nucleic acid amplification-based microfluidic devices for clinical microbial detection
,”
Chemosensors
10
,
123
(
2022
).
80.
D.
Chang
et al, “
Functional nucleic acids for pathogenic bacteria detection
,”
Acc. Chem. Res.
54
(
18
),
3540
3549
(
2021
).
81.
Y.
Chi
et al, “
Single bacteria detection by droplet DNAzyme-coupled rolling circle amplification
,”
Anal. Methods
14
(
23
),
2244
2248
(
2022
).
82.
M.
Azizi
et al, “
Pathogenic bacteria detection using RNA-based loop-mediated isothermal-amplification-assisted nucleic acid amplification via droplet microfluidics
,”
ACS Sens.
4
(
4
),
841
848
(
2019
).
83.
Z.
Li
et al, “
Automated microfluidic nucleic acid detection platform-integrated RPA-T7-Cas13a for pathogen diagnosis
,”
Anal. Chem.
95
(
17
),
6940
6947
(
2023
).
84.
K.
Dodo
,
K.
Fujita
, and
M.
Sodeoka
, “
Raman spectroscopy for chemical biology research
,”
J. Am. Chem. Soc.
144
(
43
),
19651
19667
(
2022
).
85.
K. S.
Menghrajani
et al, “
Probing vibrational strong coupling of molecules with wavelength-modulated Raman spectroscopy
,”
Adv. Opt. Mater.
10
(
3
),
2102065
(
2022
).
86.
R.
Esteban
,
J. J.
Baumberg
, and
J.
Aizpurua
, “
Molecular optomechanics approach to surface-enhanced Raman scattering
,”
Acc. Chem. Res.
55
(
14
),
1889
1899
(
2022
).
87.
M.
Fan
,
G. F. S.
Andrade
, and
A. G.
Brolo
, “
A review on recent advances in the applications of surface-enhanced Raman scattering in analytical chemistry
,”
Anal. Chim. Acta
1097
,
1
29
(
2020
).
88.
P.
Wang
et al, “
Recent advances in dual recognition based surface enhanced Raman scattering for pathogenic bacteria detection: A review
,”
Anal. Chim. Acta
1157
,
338279
(
2021
).
89.
X.
Yu
et al, “
All-fiber online Raman sensor with enhancement via a Fabry–Perot cavity
,”
Opt. Lett.
45
(
20
),
5760
5763
(
2020
).
90.
J.
Xia
et al, “
Application of SERS in the detection of fungi, bacteria and viruses
,”
Nanomaterials
12
,
3572
(
2022
).
91.
J.
Chen
et al, “
Progress of microfluidics combined with SERS technology in the trace detection of harmful substances
,”
Chemosensors
10
,
449
(
2022
).
92.
K.-W.
Chang
et al, “
Antibiotic susceptibility test with surface-enhanced Raman scattering in a microfluidic system
,”
Anal. Chem.
91
(
17
),
10988
10995
(
2019
).
93.
B.
Krafft
et al, “
Microfluidic device for concentration and SERS-based detection of bacteria in drinking water
,”
Electrophoresis
42
(
1–2
),
86
94
(
2021
).
94.
F.
Safir
et al, “
Combining acoustic bioprinting with AI-assisted Raman spectroscopy for high-throughput identification of bacteria in blood
,”
Nano Lett.
23
(
6
),
2065
2073
(
2023
).
95.
C.-C.
Liao
et al, “
A microfluidic microwell device operated by the automated microfluidic control system for surface-enhanced Raman scattering-based antimicrobial susceptibility testing
,”
Biosens. Bioelectron.
191
,
113483
(
2021
).
96.
Ü.
Dogan
et al, “
Escherichia coli enumeration in a capillary-driven microfluidic chip with SERS
,”
Biosensors
12
,
765
(
2022
).
97.
L.
Shang
et al, “
Stable SERS detection of lactobacillus fermentum using optical tweezers in a microfluidic environment
,”
Anal. Chem.
96
,
248
255
(
2023
).
98.
L.
Tan
et al, “
Surface-enhanced Raman spectroscopy: A novel diagnostic method for pathogenic organisms
,”
Vib. Spectrosc.
127
,
103560
(
2023
).
99.
Q.
Wei
,
Q.
Dong
, and
H.
Pu
, “
Multiplex surface-enhanced Raman scattering: An emerging tool for multicomponent detection of food contaminants
,”
Biosensors
13
,
296
(
2023
).
100.
N.
Pazos-Perez
et al, “
Ultrasensitive multiplex optical quantification of bacteria in large samples of biofluids
,”
Sci. Rep.
6
(
1
),
29014
(
2016
).
101.
S.
Asgari
et al, “
Duplex detection of foodborne pathogens using a SERS optofluidic sensor coupled with immunoassay
,”
Int. J. Food Microbiol.
383
,
109947
(
2022
).
102.
X.
Zhou
et al, “
Bacteria detection: From powerful SERS to its advanced compatible techniques
,”
Adv. Sci.
7
(
23
),
2001739
(
2020
).
103.
T.
Gong
et al, “
Development of SERS tags for human diseases screening and detection
,”
Coord. Chem. Rev.
470
,
214711
(
2022
).
104.
C.
Wang
et al, “
Highly-efficient SERS detection for E. coli using a microfluidic chip with integrated NaYF4:Yb,Er@SiO2@Au under near-infrared laser excitation
,”
Microsyst. Technol.
27
(
9
),
3285
3291
(
2021
).
105.
L.
Rodríguez-Lorenzo
et al, “
Gold nanostars for the detection of foodborne pathogens via surface-enhanced Raman scattering combined with microfluidics
,”
ACS Appl. Nano Mater.
2
(
10
),
6081
6086
(
2019
).
106.
S.
Asgari
et al, “
Separation and detection of E. coli O157:H7 using a SERS-based microfluidic immunosensor
,”
Microchim. Acta
189
(
3
),
111
(
2022
).
107.
J.
Zhuang
et al, “
SERS-based CRISPR/Cas assay on microfluidic paper analytical devices for supersensitive detection of pathogenic bacteria in foods
,”
Biosens. Bioelectron.
207
,
114167
(
2022
).
108.
A.
Teixeira
et al, “
Multifuntional gold nanoparticles for the SERS detection of pathogens combined with a LAMP-in-microdroplets approach
,”
Materials
13
,
1934
(
2020
).
109.
W.
Lu
et al, “
Combination of an artificial intelligence approach and laser tweezers Raman spectroscopy for microbial identification
,”
Anal. Chem.
92
(
9
),
6288
6296
(
2020
).
110.
F.
Weber
et al, “
Using stable isotope probing and Raman microspectroscopy to measure growth rates of heterotrophic bacteria
,”
Appl. Environ. Microbiol.
87
(
22
),
e01460
21
(
2021
).
111.
H.
Jayan
,
H.
Pu
, and
D.-W.
Sun
, “
Recent developments in Raman spectral analysis of microbial single cells: Techniques and applications
,”
Crit. Rev. Food Sci. Nutr.
62
(
16
),
4294
4308
(
2022
).
112.
G.
Azemtsop Matanfack
et al, “
Raman stable isotope probing of bacteria in visible and deep UV-ranges
,”
Life
11
,
1003
(
2021
).
113.
K. S.
Lee
et al, “
An automated Raman-based platform for the sorting of live cells by functional properties
,”
Nat. Microbiol.
4
(
6
),
1035
1048
(
2019
).
114.
S.
Kwon
and
S.
Lee
, “
Accurate measurement of self-diffusion coefficient of nanoparticles in aqueous solutions using a dynamic light scattering
,”
J. Korean Phys. Soc.
77
(
8
),
700
706
(
2020
).
115.
F.
Caputo
et al, “
Measuring particle size distribution and mass concentration of nanoplastics and microplastics: Addressing some analytical challenges in the sub-micron size range
,”
J. Colloid Interface Sci.
588
,
401
417
(
2021
).
116.
X.
Li
et al, “
On-line measurement for velocity and particle size distribution of flowing aerosol by dynamic light scattering
,”
Opt. Lasers Eng.
160
,
107271
(
2023
).
117.
M.
Tavakkoli Yaraki
and
Y. N.
Tan
, “
Recent advances in metallic nanobiosensors development: Colorimetric, dynamic light scattering and fluorescence detection
,”
Sens. Int.
1
,
100049
(
2020
).
118.
Z.
Jia
et al, “
Dynamic light scattering: A powerful tool for in situ nanoparticle sizing
,”
Colloids Interfaces
7
,
15
(
2023
).
119.
A. V.
Kabashin
,
V. G.
Kravets
, and
A. N.
Grigorenko
, “
Label-free optical biosensing: Going beyond the limits
,”
Chem. Soc. Rev.
52
(
18
),
6554
6585
(
2023
).
120.
Y.-L.
Pan
et al, “
Review of elastic light scattering from single aerosol particles and application in bioaerosol detection
,”
J. Quant. Spectrosc. Radiat. Transf.
279
,
108067
(
2022
).
121.
R.
Asor
and
P.
Kukura
, “
Characterising biomolecular interactions and dynamics with mass photometry
,”
Curr. Opin. Chem. Biol.
68
,
102132
(
2022
).
122.
A.
Gołębiowski
and
B.
Buszewski
, “
Characterization of colloidal particles of a biological and metallic nature
,”
Microchem. J.
191
,
108864
(
2023
).
123.
A.
Dorodnyy
,
J.
Smajic
, and
J.
Leuthold
, “
Mie scattering for photonic devices
,”
Laser Photonics Rev.
17
(
9
),
2300055
(
2023
).
124.
M.
Imanbekova
et al, “
Recent advances in optical label-free characterization of extracellular vesicles
,”
Nanophotonics
11
(
12
),
2827
2863
(
2022
).
125.
R.
Cerbino
,
F.
Giavazzi
, and
M. E.
Helgeson
, “
Differential dynamic microscopy for the characterization of polymer systems
,”
J. Polym. Sci.
60
(
7
),
1079
1089
(
2022
).
126.
Y.-T.
Chen
et al, “
Review of integrated optical biosensors for point-of-care applications
,”
Biosensors
10
,
209
(
2020
).
127.
N.
Yang
et al, “
Detection of airborne pathogens with single photon counting and a real-time spectrometer on microfluidics
,”
Lab Chip
22
(
24
),
4995
5007
(
2022
).
128.
S.
Hengoju
et al, “
Optofluidic detection setup for multi-parametric analysis of microbiological samples in droplets
,”
Biomicrofluidics
14
(
2
),
024109
(
2020
).
129.
W.
Huang
et al, “
Microfluidic multi-angle laser scattering system for rapid and label-free detection of waterborne parasites
,”
Biomed. Opt. Express
9
(
4
),
1520
1530
(
2018
).
130.
L.
Xu
,
X.
Bai
, and
A. K.
Bhunia
, “
Current state of development of biosensors and their application in foodborne pathogen detection
,”
J. Food Prot.
84
(
7
),
1213
1227
(
2021
).
131.
Y.
Liu
et al, “
Recent progress in microfluidic biosensors with different driving forces
,”
TrAC, Trends Anal. Chem.
158
,
116894
(
2023
).
132.
Q.
Xu
et al, “
The dual nucleic acid amplification with dynamic light scattering strategy for ultrasensitive detection of salmonella in milk
,”
Microchem. J.
184
,
108143
(
2023
).
133.
S.
Zhan
et al, “
Gold nanoflower-enhanced dynamic light scattering immunosensor for the ultrasensitive No-wash detection of Escherichia coli O157:H7 in milk
,”
J. Agric. Food Chem.
67
(
32
),
9104
9111
(
2019
).
134.
M.
Hussain
et al, “
Pseudomonas aeruginosa detection based on droplets incubation using an integrated microfluidic chip, laser spectroscopy, and machine learning
,”
Spectrochim. Acta: Part A
288
,
122206
(
2023
).
135.
D.
Petrovszki
et al, “
An integrated electro-optical biosensor system for rapid, low-cost detection of bacteria
,”
Microelectron. Eng.
239–240
,
111523
(
2021
).
136.
F.
Zorzi
et al, “
Optofluidic flow cytometer with in-plane spherical mirror for signal enhancement
,”
Sensors
23
,
9191
(
2023
).
137.
S.
Lee Kwang
et al, “
Rapid bacterial detection in urine using laser scattering and deep learning analysis
,”
Microbiol. Spectrum
10
(
2
),
e01769
21
(
2022
).
138.
M.
Hussain
et al, “
Rapid detection of pseudomonas aeruginosa based on lab-on-a-chip platform using immunomagnetic separation, light scattering, and machine learning
,”
Anal. Chim. Acta
1189
,
339223
(
2022
).
139.
M.
Wang
et al, “
Developments of conventional and microfluidic flow cytometry enabling high-throughput characterization of single cells
,”
Biosensors
12
,
443
(
2022
).
140.
G.
Konoplev
et al, “
Label-free physical techniques and methodologies for proteins detection in microfluidic biosensor structures
,”
Biomedicines
10
,
207
(
2022
).
141.
S. K.
Filippov
et al, “
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
(
2023
).
142.
J.
Chen
et al, “
A universal boronate-affinity crosslinking-amplified dynamic light scattering immunoassay for point-of-care glycoprotein detection
,”
Angew. Chem. Int. Ed.
61
(
7
),
e202112031
(
2022
).
143.
Y.
Suo
et al, “
A large-scale pico-droplet array for viable bacteria digital counting and dynamic tracking based on a thermosetting oil
,”
Analyst
147
(
14
),
3305
3314
(
2022
).
144.
M.
Morani
et al, “
Development of a microfluidic droplet platform with an antibody-free magnetic-bead-based strategy for high through-put and efficient EVs isolation
,”
Talanta
249
,
123625
(
2022
).
145.
X.
Hao
et al, “
Aptamer surface functionalization of microfluidic devices using dendrimers as multi-handled templates and its application in sensitive detections of foodborne pathogenic bacteria
,”
Anal. Chim. Acta
1056
,
96
107
(
2019
).
146.
L.
Litti
et al, “
3D printed microfluidic device for magnetic trapping and SERS quantitative evaluation of environmental and biomedical analytes
,”
ACS Appl. Mater. Interfaces
13
(
29
),
34752
34761
(
2021
).
You do not currently have access to this content.