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Interferometry

Interferometry is a powerful technique used to measure and analyze the interference patterns created by the interaction of two or more waves.
This non-destructive method has a wide range of applications in fields such as optics, metrology, and astronomy.
By studying the interference patterns, researchers can extract valuable information about the properties of the wavefronts, including their phase, amplitude, and frequency.
Interferometry enables highly precise measurements and is crucial for advances in areas like high-resolution imaging, surface profiling, and gravitational wave detection.
Optimizing interferometry research can be streamlined with PubCompare.ai, which leverages AI-driven comparisons to identify the most reliable and effective protocols from literature, preprints, and patents.
This tool can enhance reproducibility and accuracy, empowering researchers to discover the full potentil of interferometry and push the boundaries of scientific discovery.

Most cited protocols related to «Interferometry»

1
Twin-Image Elimination in Holographic Microscopy
The first method falls under the broad category of Interferometric Phase-Retrieval Techniques and is applicable to cases where the recorded intensity is dominated by the holographic diffraction terms.31 (link)–33 The first step is the digital reconstruction of the hologram, which is achieved by propagating the hologram intensity by a distance of z2 away from the hologram plane yielding the initial wavefront Urec. As a result of this computation, the virtual image of the object is recovered together with its spatially overlapping defocused twin-image. It is important to note that the recorded intensity can also be propagated by a distance of −z2. In this case, the real image of the object can be recovered, while the defocused virtual image leads to the twin-image formation.
Due to the small cell-sensor distance in the incoherent holographic microscopy scheme presented here, the twin-image may carry high intensities, especially for relatively large objects like white blood cells. In such cases, the fine details inside the micro-objects may get suppressed. Similarly, the twin-images of different cells which are close to each other get superposed, leading to an increase in background noise. This issue is especially pronounced for microscopy of dense cell solutions, where the overlapping twin images of many cells lowers the counting accuracy due to reduced SNR.
In order to eliminate the twin-image artifact, an iterative approach using finite support constraints is utilized.33 Basically, this technique relies on the fact that duplicate information for the phase and amplitude of the object exists in two different reconstruction planes at distances +z2 and −z2 from the hologram plane, where the virtual and real images of the object are recovered, respectively. Therefore, a twin-image-free reconstruction in one of the image planes can be obtained, while filtering out the duplicate image in the other plane. Without loss of generality, we have chosen to filter out the real image to obtain a twin-image-free reconstruction in the virtual image plane at −z2. Due to the finite size of the micro-objects, the real image of the object only occupies the region inside its support, while the defocused twin-image image spreads out to a wider region around the object, also overlapping with the real image inside the support. Hence, deleting the information only inside the support ensures that the real image is completely removed from the reconstructed wavefront. Nevertheless, the virtual image information inside the support is also lost, and the iterative technique tries to recover the missing information of the virtual image by going back and forth between the virtual and real image planes, recovering more of the lost information at each iteration. The success of this algorithm is highly dependent on the Fresnel number of the recording geometry, which is given by Nf = n(object size)2/(λz). It is reported that the technique proves successful for Fresnel numbers as high as 10.33 For RBCs of approximately 7µm diameter, the typical recording geometries presented here involve Fresnel numbers of <0.2; hence, the twin-image elimination method yields highly satisfactory results.
The steps of twin-image elimination are detailed below:

Initially the real image, which is the back-projected hologram at a distance of +z2, is used for determining the object support. Object support can be defined by either thresholding the intensity of the reconstructed image, or searching for its local minima.

The region inside the support is deleted and a constant value is assigned to this region as an initial guess for the deleted part of the virtual image inside the support as shown below: Uz2(i)(x,y)={Urec,x,y∉SU¯rec,x,y∈S where Uz(i)(x,y) denotes the field at the real image plane after the ith iteration. S represents the area defined by the object support, and Ūrec is the mean value of Urec within the support.

Then, the field at the real image plane is back propagated by −2z2 to the virtual image plane. Ideally, the reconstruction at this plane should be free from any twin-image distortions. Therefore, the region outside the support can be set to a constant background value to eliminate any remaining out-of-focus real image in the virtual image plane. However, this constraint is applied smoothly as determined by the relaxation parameter β below, rather than sharply setting the image to d.c. level outside the support: U−z2(i)(x,y)={D−D−U−z2(i)β,x,y∉SU−z2    (i)   ,x,y∈S where D is the background in the reconstructed field, which can either be obtained from a measured background image in the absence of the object, or can simply be chosen as the mean value of the field outside the object supports at the virtual image plane. β is a real valued parameter greater than unity, and is typically chosen around 2–3 in this article. Increasing β leads to faster convergence, but compromises the immunity of the iterative estimation accuracy to background noise.

The field at the virtual image plane is forward propagated to the real-image plane, where the region inside the support now has a better estimate of the missing part of the virtual image. The region outside the support can be replaced by Uz2(1)(x,y), the original reconstructed field at the real image plane, as shown below: Uz2(i+1)(x,y)={Uz2(1),x,y∉SUz2(i+1),x,y∈S

Steps c to d can be repeated iteratively until the final image converges. In most cases in this article, convergence is achieved after 10–15 iterations. This iterative computation takes around 4 seconds for an image size of ~5 Mpixels using a regular CPU (central processing unit – e.g., Intel Q8300) and it gets >40× faster using a GPU (graphics processing unit – e.g., NVIDIA GeForce GTX 285) achieving <0.1 sec computation time for the same image size.

Mudanyali O., Tseng D., Oh C., Isikman S.O., Sencan I., Bishara W., Oztoprak C., Seo S., Khademhosseini B, & Ozcan A. (2010). Compact, Light-weight and Cost-effective Microscope based on Lensless Incoherent Holography for Telemedicine Applications. Lab on a chip, 10(11), 1417-1428.
Publication 2010
Erythrocytes Holography Interferometry Leukocytes Microscopy Neoplasm Metastasis Reconstructive Surgical Procedures Response, Immune Twins
2
Electrochemical Characterization of Redox Species
All chemicals used were analytical grade and were used as received from Sigma-Aldrich (Irvine, UK) without any further purification. All solutions were prepared with deionised water of resistivity no less than 18.2 MΩ cm and were vigorously degassed prior to electrochemical measurements with high purity, oxygen free nitrogen. The tested solutions were: 1 mM N,N,N′,N′-tetramethyl-p-phenylenediamine (TMPD) in 0.1 M KCl, 1 mM Ru(NH3)6Cl33+/2+ (RuHex) in 0.1 M KCl, 1 mM Dopamine in pH 7 phosphate buffer solution/0.1 M KCl (PBS), 1 mM β-Nicotinamide adenine dinucleotide (NADH) in pH 7 PBS/0.1 M KCl, 1 mM Capsaicin in 0.1 M HPO4, and 1 mM Ascorbic acid in pH 7 PBS/0.1 M KCl. The following diffusion coefficients were used in this work (cm2 s−1): 6.74 × 10−6 for dopamine [21 (link)], 7.40 × 10−6 for NADH [22 (link)], 9.1 × 10−6 for RuHex [23 (link)], 6.32 × 10−6 for TMPD [24 (link)], 7.03 × 10−6 for capsaicin, and 1.42 × 10−6 for ascorbic acid [25 (link)].
Electrochemical measurements were performed using an Autolab PGSTAT204 (Metrohm Autolab, Utrecht, The Netherlands) computer-controlled potentiostat. All measurements were conducted using a three-electrode system with a Pt wire counter electrode, a saturated calomel electrode (SCE) reference electrode, and screen-printed graphite working electrodes (SPEs) completing the circuit. The SPEs were fabricated in-house with appropriate stencil designs to achieve a 3.1 mm diameter working electrode, using a carbon-graphite ink (Product Ink: C2000802P2; Gwent Electronic Materials Ltd., Pontypool, UK) printed using a DEK 248 screen printer machine (DEK, Weymouth, UK) onto a polyester (Autostat, Milan, Italy, 250 micron thickness) flexible film. This layer was cured in a fan oven at 60 °C for 30 min and finally, a dielectric paste (Product Code: D2070423D5; Gwent Electronic Materials Ltd., Pontypool, UK) was then printed onto the polyester substrate to cover the connections. After a second curing process at 60 °C for 30 min, the SPEs are ready to be used. The in-house fabricated SPEs have been previously reported and characterized [26 (link),27 (link)]. The electrode’s roughness was calculated using a Profilm3D white light interferometry (WLI) (Filmetric, San Diego, CA, USA).
The electrode areas calculated using the Randles–Ševćik equation and cyclic voltammetry were undertaken with 10 different scan rates (5, 10, 15, 25, 50, 75, 100, 150, 250, and 500 mV s−1). For the area calculated using the Anson equation and chronocoulometric experiments, CC, two potentials were applied. The first potential was applied at a low voltage where no electrochemical Faradaic reaction occurred, and the second potential was applied in order detect the corresponding Faradaic process; total charge passed versus time was recorded for 6 s.
García-Miranda Ferrari A., Foster C.W., Kelly P.J., Brownson D.A, & Banks C.E. (2018). Determination of the Electrochemical Area of Screen-Printed Electrochemical Sensing Platforms. Biosensors, 8(2), 53.
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Publication 2018
Ascorbic Acid Buffers calomel Capsaicin Carbon Coenzyme I Diffusion Dopamine Graphite Interferometry Light Nitrogen Oxygen Paste Phosphates Polyesters Radionuclide Imaging SpeA protein, Streptococcus pyogenes tetramethyl-p-phenylenediamine
3
Biomolecular Condensate Formation Assays
Full details of all procedures, including specific compounds used, specific details surrounding phase separation assays, biolayer interferometry, FRAP assays, droplet fusion experiments, partitioning assays, SXT, and coarse-grained simulations are provided in SI Appendix.
For phase separation assays, polyanions were diluted to 1 µg/µL in 100 mM K2HPO4/KH2PO4 buffer at pH 7. Peptides were added at 250-µM concentrations. For partitioning experiments, probe molecules were spiked in at 100 nM. For the generation of pure RNA liquid droplets, homopolymeric RNAs were diluted to 2 µg/µL in 1x PBS buffer with 30% PEG and 10 mM MgCl2 at pH 7. For intradroplet FRAP, a circular area of 1-μM radius was bleached in droplets with a radius between 5 μM and 10 μM. For SXT 3D reconstructions, 92 projection images, with 200-ms exposure time each, were acquired sequentially around a rotation axis with 2° increment angles. For droplet fusion assays, confocal time-lapse images were taken of droplets settling on the coverslip and fusing together. Coarse-grained simulations are lattice-based and utilize a simple neighbor−neighbor interaction potential. For all other details, please see SI Appendix.
Boeynaems S., Holehouse A.S., Weinhardt V., Kovacs D., Van Lindt J., Larabell C., Van Den Bosch L., Das R., Tompa P.S., Pappu R.V, & Gitler A.D. (2019). Spontaneous driving forces give rise to protein−RNA condensates with coexisting phases and complex material properties. Proceedings of the National Academy of Sciences of the United States of America, 116(16), 7889-7898.
Publication 2019
Biological Assay Buffers Epistropheus Interferometry Magnesium Chloride Peptides polyanions potassium phosphate, dibasic Radius Reconstructive Surgical Procedures RNA
4
Biolayer Interferometry Binding Assay for HA0
The binding assay was performed by biolayer interferometry (BLI) using an Octet Red instrument (ForteBio, Menlo Park, CA). Biotinylated HA0 at approximately 10–50 μg ml−1 in 1 × kinetics buffer (1 × PBS with 0.01% BSA and 0.002% Tween 20) was loaded onto streptavidin biosensors and incubated with supernatant from transfected cells or with the indicated concentration of Fab. Streptavidin biosensors that were not loaded were used as a reference for subtracting background binding from signals. Briefly, the assay consisted of five steps: (1) baseline: 60 s with 1 × kinetics buffer; (2) loading: 120 s with biotinylated HA0; (3) baseline: 60 s with 1 × kinetics buffer; (4) association: 120 s with samples (supernatant from transfected cells or purified Fab); and (5) dissociation: 120 s with 1 × kinetics buffer. For binding assay that used supernatant from transfected cells, relative Fab concentrations were determined by western blot using a monoclonal antibody to the His-tag (catalogue number: MAB230P, Maine Biotechnology, Portland, ME) as the primary antibody, anti-mouse goat antibody (catalogue number: 115-035-008, Jackson ImmunoResearch, West Grove, PA) as the secondary antibody, and subsequent densitometry analysis using ImageJ. Since the concentration of C05 Fab in the supernatant was unknown, we were not able to calculate the exact Kd. Instead, we normalized the apparent Kd to that of WT C05 Fab (VVSAGW) and further normalized to the expression level to estimate the relative Kd. The relative Kd of WT C05 Fab was set as 1. For estimating the exact Kd, a 1:1 binding model was used. In cases where the binding affinity was relatively weak (Kd>300 nM), a 1:1 binding model did not fit well due to the contribution of non-specific binding to the response curve. Subsequently, a 2:1 heterogeneous ligand model was used to improve the fitting.
Wu N.C., Grande G., Turner H.L., Ward A.B., Xie J., Lerner R.A, & Wilson I.A. (2017). In vitro evolution of an influenza broadly neutralizing antibody is modulated by hemagglutinin receptor specificity. Nature Communications, 8, 15371.
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Publication 2017
Antibodies, Anti-Idiotypic Biological Assay Biosensors Buffers Cells Debility Densitometry Genetic Heterogeneity Goat Immunoglobulins Interferometry Kinetics Ligands Monoclonal Antibodies Mus Streptavidin Tween 20 Western Blotting
5
Correcting Posterior Segment OCT Images

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Publication 2013
Chambers, Anterior Cornea Epistropheus Hyperopia Interferometry Lens, Crystalline Light Posterior Eye Segment Pupil Radionuclide Imaging Radius Retina Vision

Most recents protocols related to «Interferometry»

1
Enhancing EBOV GP Trimer Stability
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Example 3

We hypothesized that HR1C is essential to EBOV GP metastability. Since HR1C in wildtype EBOV GP is equivalent in length (8 aa) to a truncated HR1N in the prefusion-optimized HIV-1 Env, metastability in EBOV GP may not be sensitive to the HR1C length and likely requires a different solution. We thus hypothesized that a proline mutation in HR1C, termed P1-8, may rigidify the HR1C bend and improve the EBOV GP trimer stability.

To examine this possibility, eight GPΔmuc-W615L variants, each bearing a proline mutation in HR1C but without the L extension and foldon at the C terminus, were validated experimentally. All constructs were transiently expressed in 250-ml 293 F cells and purified using an mAb114 column, which captures all GP species. The proline mutation at most positions in HR1C showed little effect on the composition of GP species except for T577P (P2) and L579P (P4), which displayed notable trimer peaks at ˜11 ml in the SEC profiles. In a separate experiment, all eight constructs were transiently expressed in 250-ml 293 F cells and purified using an mAb100 column. Only P2 and P4 showed any measurable trimer yield, with a notably high SEC peak observed for P4 that corresponds to well-formed trimers. The mAb100-purified GP was also analyzed by BN-PAGE, which showed a trimer band for P2 and P4. Overall, the T577P mutation, P2, can substantially increase trimer yield, while the L579P mutation, P4, exhibited a less pronounced effect.

Next, the T577P mutation (P2) was incorporated into the GPΔmuc-WL2-foldon construct, resulting in a construct named GPΔmuc-WL2P2-foldon. This construct was expressed transiently in 1-liter 293 F cells and purified using an mAb100 column for SEC characterization on a HiLoad Superdex 200 16/600 GL column. In three production runs, GPΔmuc-WL2P2-foldon generated a trimer peak that was two- and four-fold higher than GPΔmuc-WL2-foldon and wildtype GPΔmuc-foldon, respectively, with an average yield of 2.6 mg after SEC. Protein collected in the SEC range of 55.5-62.0 ml was analyzed by BN-PAGE, which displayed a trimer band across all fractions without any hint of impurity. The thermostability of GPΔmuc-WL2P2-foldon was determined by DSC after mAb100 and SEC purification.

Unexpectedly, two transition peaks were observed in the thermogram, one registered at a lower Tm of 61.6° C. and the other at a higher Tm of 68.2° C. To this end, a second construct bearing the L579P mutation (P4), termed GPΔmuc-WL2P4-foldon, was also assessed by DSC. Although only one peak was observed in the thermogram with a Tm of 67.0° C., a slight widening at the onset of the peak suggested a similar unfolding behavior upon heating. DSC thus revealed the complexity associated with a proline-rigidified HR1C bend, which may increase the trimer yield at the cost of reducing GP thermostability. The antigenicity of GPΔmuc-WL2P2-foldon was assessed using the same panel of 10 antibodies by ELISA (FIG. 3F-G) and bio-layer interferometry (BLI). The T577P mutation (P2) appeared to improve GP binding to most antibodies with respect to GPΔmuc-WL2-foldon (FIG. 3G), with a 40% reduction in EC50 observed for bNAb BDBV223, which targets HR2-MPER. Although BLI profiles were almost indistinguishable between wildtype and redesigned GPΔmuc-foldon trimers—all with fast on-rates and flattened dissociation curves, a two-fold higher signal at the lowest concentration (12.5 nM) was observed for GPΔmuc-WL2P2-foldon binding to bNAb BDBV223, consistent with the ELISA data.

Our results thus demonstrated the importance of HR1C to EBOV GP metastability and an unexpected sensitivity of HR1C to proline mutation. Recently, Rutten et al. tested proline mutations in HR1C along with a K588F mutation to stabilize filovirus GP trimers (Cell Rep. 30, 4540-50, 2020). While a similar pattern of increased trimer yield was observed for the T577P mutant, the reported thermostability data appeared to be inconsistent with our DSC measurement. Further investigation is warranted to fully understand the role of HR1C in filovirus-cell fusion and its impact on GP stability.

US11872277B2. Compositions and methods related to ebolavirus vaccines (2024-01-16). THE SCRIPPS RESEARCH INSTITUTE [US]. Inventors: Linling He [US], Jiang Zhu [US], Anshul Chaudhary [US], Ian Wilson [US].
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Patent 2024
Antibodies Antigens Broadly Neutralizing Antibodies Cells Decompression Sickness Enzyme-Linked Immunosorbent Assay Filoviridae Fusions, Cell HIV-1 Hypersensitivity Interferometry mAb114 Mutation Proline Proteins Thermography
2
SARS-CoV-2 Spike Protein Binding Assay
Binding competition assay was carried out using biolayer interferometry (Octet Red348, Sartorius, USA), as described previously (49 (link)). In brief, SARS-CoV-2 S ectodomain trimer (50 µg/ml) was immobilized onto the Protein A biosensor (Sartorius, USA) via an anti-Streptag mAb (IBA). After a brief washing step in PBS, the biosensors were dipped into a well containing the primary HCAb (50 µg/ml) for 15 min, followed by a short washing step in PBS. Subsequently the biosensors were immersed into a well containing the HCAb 2 (50 µg/ml) for 15 min.
Du W., Janssens R., Mykytyn A.Z., Li W., Drabek D., van Haperen R., Chatziandreou M., Rissmann M., van der Lee J., van Dortmondt M., Martin I.S., van Kuppeveld F.J., Hurdiss D.L., Haagmans B.L., Grosveld F, & Bosch B.J. (2023). Avidity engineering of human heavy-chain-only antibodies mitigates neutralization resistance of SARS-CoV-2 variants. Frontiers in Immunology, 14, 1111385.
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Publication 2023
Biological Assay Biosensors Interferometry SARS-CoV-2 Staphylococcal Protein A
3
Mass Photometry Analysis of NET1-Glycosaminoglycan Complexes
Prior to measurement, glass slides (24 × 50 mm #1.5 special, Menzel-Gläser) were reacted with 3-aminopropyl-triethoxysilane (APTES) to produce an amino-silane functionalized surface. Slides were plasma cleaned under O2, then dipped in a 5% APTES solution in acetone. Excess APTES was removed by rinsing in acetone. The slides were then baked at 110 °C before washing with isopropanol. 5 μM of either apo-NET1ΔC or NET1ΔC with equimolar HO-dp8 or HO-dp10 were incubated overnight in 50 mM tris pH 8.5, 200 mM NaCl at 4 °C. 0.2 μl of NET1ΔC-dp8 or NET1ΔC-dp10 were diluted in 9.8 μl of buffer directly on APTES functionalized glass slides in a 3 mm diameter 1 mm deep culture well gasket (Grace Bio-Labs), for a final concentration of 100 nM. Interferometric videos were taken on a prototype mass photometry system and processed as described32 (link). Briefly, a 477 nm laser focussed to 1.5 μm was passed through a beam splitter and swept across the sample to generate an interferometric video of the buffer-glass interface. Frames were collected at 1000 Hz, and a differential video generated by subtracting each frame from the previous. In these videos, the interaction of the protein with the glass is clearly visible as a spot, where the intensity of the spot is directly proportional to the protein mass. Pixels were pre-binned 3 × 3 and frames were fivefold time averaged during acquisition, giving a final pixel size of 70.2 nm and a frequency of 200 Hz. During processing, videos were subjected to a further fivefold frame averaging. Masses were calculated via a calibration curve using alcohol dehydrogenase, β-amylase, and Protein A. For NET1ΔC w/o GAG, a total count of 8072 were measured with a monomer population of 92.99%. For the NETΔC-dp8 complex, 7192 counts revealed a monomer-dimer ratio of 86.37% to 12.39%. The NET1ΔC-dp10 complex was studied with 17075 total counts revealing a monomer-hexamer ratio of 79.45% to 3.38%.
Meier M., Gupta M., Akgül S., McDougall M., Imhof T., Nikodemus D., Reuten R., Moya-Torres A., To V., Ferens F., Heide F., Padilla-Meier G.P., Kukura P., Huang W., Gerisch B., Mörgelin M., Poole K., Antebi A., Koch M, & Stetefeld J. (2023). The dynamic nature of netrin-1 and the structural basis for glycosaminoglycan fragment-induced filament formation. Nature Communications, 14, 1226.
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Publication 2023
3-(triethoxysilyl)propylamine Acetone Amylase Buffers Dehydrogenase, Alcohol Interferometry Isopropyl Alcohol Photometry Plasma Proteins Proto-Oncogene Mas Reading Frames Silanes Sodium Chloride Staphylococcal Protein A Tromethamine
4
Biolayer Interferometry Quantifies Protein-Degrader Binding Kinetics
We performed biolayer interferometry (BLI) with an OctetRED384 system (Data Acquisition Version 9.0.0.49) as described previously40 (link). Briefly, biotinylated proteins (Ligands) were diluted to 12.5 μg ml–1 in Octet buffer (20 mM HEPES pH 8.0, 150 mM NaCl, 0.02% Tween-20, and 0.02% BSA). Ligands were loaded onto streptavidin biosensors (ForteBio, 18-5019) for 120 s, washed in Octet buffer for 200 s, quenched with 50 μM d-biotin (Invitrogen) for 120 s, and washed in Octet buffer for 500 s.
When cIAP1BUCR1 was the Ligand and BTKKD was the analyte, biotinylated cIAP1 protein was dipped into 1 μM degrader for 60 s followed by a wash for 500 s in Octet buffer. When BTKKD~BCCov was the ligand and cIAP1BUCR1 the analyte, no binary protein-degrader formation step was needed. Instead, BTKKD was incubated separately with 4x molar excess BCCov until completely covalently modified (as determined by mass spectrometry). Excess compound was desalted using a Zeba Spin Desalting Column (Cat. #89882). The preformed binary complex was then dipped into varying concentrations of analyte diluted in Octet buffer for 300 s to determine kon, followed by a wash for 500 s to determine koff. Binding kinetics were calculated and plotted using BiaEvaluation Software (version 4.1.1). Cooperativity (α) was determined by calculating the ratio of binary Kd to ternary complex Kd.
Schiemer J., Maxwell A., Horst R., Liu S., Uccello D.P., Borzilleri K., Rajamohan N., Brown M.F, & Calabrese M.F. (2023). A covalent BTK ternary complex compatible with targeted protein degradation. Nature Communications, 14, 1189.
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Publication 2023
Baculoviral IAP Repeat-Containing 2 Protein Biosensors Biotin Buffers HEPES Interferometry Kinetics Ligands Mass Spectrometry Molar Protein Biosynthesis Proteins Sodium Chloride Streptavidin Tween 20
5
Seismic Network Noise Correlation Workflow
The seismic instrumentation in Australia has increased gradually over the years from tens of stations in the early 1990s to ~300 active stations in recent years (supplementary Fig. S1a). Among these stations, several long operating networks (e.g., AU and S1) form the backbone array, which are augmented by temporary deployments with an operation period of 1–2 years in target regions across the continent (Figure S1b; also see Fig. 1b). The C2 workflow is well suited for networks in Australia where permanent stations are distributed near the coastal areas, surrounding the temporary deployments further inland (see Fig. 1b). We briefly summarize the key processing steps employed to extract the noise correlation functions (NCFs) between synchronous and asynchronous stations. As a first step, conventional ambient noise correlation (i.e., C1) is conducted between synchronous station pairs. The continuous seismic recordings are cut into 1-hour segments with a 30-min overlap between consecutive time windows. After removing the mean and linear trends, we apply a low-pass filter with a corner frequency of 1.25 Hz and downsample the data to 2.5 Hz. The amplitude spectra of traces are normalized (i.e., spectral whitening) to broaden the frequency content. A daily NCF is obtained by cross-correlating the preprocessed segments and stacking the resulting cross-correlation functions from all (48) time windows. Similarly, daily NCFs are stacked to form a monthly stack. With an ensemble of monthly stacks, we conduct quality control by examining the consistency of NCFs, whereby correlation coefficients between all NCF pairs are computed and those with a below-average value are discarded. The accepted NCFs are stacked to obtain the final NCFs (i.e., C1 functions), which form the basis for bridging asynchronous stations using the C2 approach.
The C2 workflow invokes source-receiver interferometry (SRI) to project the energy from one receiver via the surrounding backbone arrays to the other receiver41 (link). The application of C2 does not directly cross-correlate the noise recordings between the two target receivers, hence simultaneous operations of the two stations are not required. This idea can be applied to effectively tie asynchronous arrays (supplementary Figure S2). For two temporary arrays deployed at different time periods, we are able to retrieve the inter-array NCFs functions with the aid of the surrounding long-term stations via a three-step process. First, the C1 is computed between temporary array A and the surrounding stations (supplementary Fig. S2a). Second, temporary array B, which is deployed after the extraction of temporary array A, is cross-correlated with the same set of stations. These two steps effectively turn the surrounding long-term stations into common virtual sources that illuminate both temporary arrays (supplementary Fig. S2b). Finally, for a target station pair, the two C1 functions from the same virtual source are cross-correlated again to form a C2 function, and all C2 functions, each corresponding to a different virtual source, are stacked to obtain a final C2 estimate. We perform a weighted stacking scheme based on the Voronoi cell tessellation and implement radial and azimuthal tapering as proposed in ref. 42 (link) to improve the stacking. We refer readers to ref. 22 for implementation details. The C2 workflow thus provides an indirect approach to retrieve the NCFs between asynchronous stations (or arrays), a situation that cannot be achieved with the conventional C1 approach. The additional ray paths from C2 connect asynchronous stations and provide complementary information to the C1 dataset (supplementary Fig. S3a; also see Fig. 2). The waveforms of C1 and C2 both show clear surface wave energy with a similar move-out over a large (0–3500 km) distance range (supplementary Fig. S3b, c). We obtain a total of 230,788 and 696,046 NCFs from C1 and C2, respectively.
Chen Y., Saygin E., Kennett B., Qashqai M.T., Hauser J., Lumley D, & Sandiford M. (2023). Next-generation seismic model of the Australian crust from synchronous and asynchronous ambient noise imaging. Nature Communications, 14, 1192.
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Publication 2023
A-A-1 antibiotic Cells Interferometry Strains Toxic Epidermal Necrolysis Vertebral Column

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Octet red96 by Molecular Devices
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Octet red96 system by Molecular Devices
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Blitz system by Molecular Devices
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The BLItz system is a label-free, real-time molecular interaction analysis instrument that measures binding kinetics and affinity. It utilizes biolayer interferometry (BLI) technology to monitor biomolecular interactions in real-time without the need for labeling. The BLItz system provides a compact and versatile solution for a wide range of applications, including protein-protein, protein-small molecule, and protein-nucleic acid interactions.
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Octet red96 instrument by Molecular Devices
Sourced in United States, Germany
The Octet RED96 instrument is a label-free, real-time analysis system used for biomolecular interaction studies. It measures binding kinetics and affinity between a wide range of molecules, including proteins, peptides, small molecules, and nucleic acids. The instrument utilizes Bio-Layer Interferometry (BLI) technology to monitor the interaction of biomolecules in real-time.
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Octet red instrument by Molecular Devices
Sourced in United States
The Octet Red instrument is a label-free, real-time detection system that utilizes biolayer interferometry technology to measure biomolecular interactions. It provides quantitative analysis of protein-protein, protein-small molecule, and protein-cell interactions without the need for labeling.

More about "Interferometry"

Interferometry: Unlocking the Power of Wave Interference Measurement.
Interferometry is a non-destructive, highly precise technique used to analyze the interaction of multiple waves, unlocking a wealth of information about their properties.
This powerful method has a wide range of applications in optics, metrology, astronomy, and beyond.
By studying the interference patterns created, researchers can extract valuable data on wavefront phase, amplitude, and frequency.
Optimize your interferometry research with AI-driven comparisons using tools like PubCompare.ai, which can help you identify the most reliable and effective protocols from literature, preprints, and patents.
Enhance reproducibility and accuracy, and discover the full potential of interferometry to push the boundaries of scientific discovery.
Explore related technologies like the Octet RED96, Octet RED384, and BLItz system, which leverage interferometry principles for biomolecular interaction analysis.
Harness the power of wave interference measurement and unlock new frontiers with the latest advancements in interferometry research.