867results about "Particle separator tubes" patented technology

Atomic object confinement apparatuses comprising periodic or quasi-periodic arrays of legs, including curved legs, and systems comprising such atomic object confinement apparatuses are provided. The atomic object confinement apparatus comprises a plurality of legs with each leg of the plurality of legs defining a one-dimensional trap segment; and a plurality of junctions with each junction of the plurality of junctions connecting at least two legs of the plurality of legs. The plurality of legs and the plurality of junctions are arranged into a periodic or quasi-periodic array of connected one-dimensional trap segments. The periodic array or quasi-periodic array comprises one or more smallest array elements. Each smallest array element of the one or more smallest array elements comprises at least one curved leg.

Disclosed are methods and systems for predictive classification of a sample's raw mass spectra, said spectra produced by mass spectrometry. Said predictive classification is performed by a trained large spectral model comprising, a self-supervised large foundational spectral model trained with unlabelled sample spectra from a plurality of sources, and fine-tuned with a task-specific model trained using labelled spectra.

Disclosed herein are spectrometer support systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, a spectrometer support apparatus may: receive, for each of a plurality of calibration samples of an analyte at different known concentrations, an array of spectrometer output intensities of the calibration sample, wherein different ones of the spectrometer output intensities in the array are associated with data representative of different deflection amounts; train a machine-learning computational model, using the plurality of known concentrations of the analyte in the calibration samples and the associated plurality of arrays of spectrometer output intensities, to output a concentration of the analyte in a target sample based on an input array of spectrometer output intensities of the target sample; and use the trained machine-learning computational model as a calibration model for the analyte for subsequent spectrometer operation.

Methods, devices, and systems for improving the quality of electrospray ionization mass spectrometer (ESI-MS) data are described, as are methods, devices, and systems for achieving improved correlation between chemical separation data and mass spectrometry data.

Disclosed are methods for detecting and quantifying target analytes in a sample by mass analysis that include detecting the presence of ions of the target analyte in the sample and quantifying the amount of the target analyte. The methods can include one or more standards (e.g., internal and / or external standards) that may be labelled (e.g., isotopically labelled).

The invention relates to the technical field of mass spectrometers, and particularly provides a multi-channel detection method of a non-continuous sample injection mass spectrometer, which comprises the following steps: feeding a plurality of compounds of a target sample into an ion mass analyzer; screening a plurality of target ions of the ion flow of the compound through a preset multi-channel screening waveform; according to the method, target ions in an ion trap are fragmented and analyzed one by one according to a sequence from low frequency to high frequency and a mass-to-charge ratio from small to large through a multi-channel resonance signal of a preset frequency band, and a mass spectrum containing a plurality of target ions is obtained. Therefore, the problems of long analysis time and low analysis efficiency due to the fact that mass spectrometry of only one target ion can be realized at a time when a non-continuous sample injection mass spectrometer in related technologies analyzes the target ions and the multiple target ions need to be analyzed one by one when the multiple target ions are analyzed are solved.

Systems and methods for determining a molecular structure include obtaining mass spectrum data for an unknown parent ion. The mass spectrum data is recorded at each collision energy of a series of collision energies and includes an abundance of one or more substructures of the unknown parent ion. The molecular structure of the unknown parent ion is determined by composing the mass spectrum data into a fragmentation profile and embedding the fragmentation profile into a reduced dimension profile. The reduced dimension profile is mapped into a fragmentation space. One or more proximate molecules in the fragmentation space are identified using the mapping and a candidate for the parent ion is identified using the one or more proximate molecules.

A non-targeted mass spectrometry identification method for tobacco carbonyl components, comprising the following steps: (1) preparing a tobacco sample extract liquid; (2) performing derivatization by using 2,4-dinitrophenylhydrazine (DNPH) to obtain a first tobacco sample; (3) replacing DNPH with DNPH-d3 for derivatization to obtain a second tobacco sample; (4) preparing a mixed tobacco sample; (5) preparing a blank sample; (6) performing UPLC-Orbitrap-HRMS analysis respectively on the first tobacco sample, the second tobacco sample, the mixed tobacco sample, and the blank sample to obtain raw LC-MS data; (7) processing the raw LC-MS data to obtain raw mass spectrum characteristic data; (8) performing multiple filtering on the raw mass spectrum characteristic data; and (9) performing structural annotation or identification on the finally reserved chromatographic peak to obtain tobacco carbonyl components. By means of multiple filtering of mass spectrum characteristic data, chromatography-mass spectrum information of noise or interference components in a raw data set is quickly eliminated, and composition information of aldehyde and ketone chemical components in a cigarette is obtained.

The invention relates to a sample injection control method and system for a mass spectrometer, and the method comprises the steps: recognizing a to-be-detected sample, and obtaining a sample recognition result; on the basis of the sample identification result, determining sample introduction parameters of the to-be-detected sample; and performing sample injection control on the to-be-detected sample according to the sample injection parameters, and introducing the to-be-detected sample into a mass spectrometer for analysis. The technical problem that the mass spectrometry efficiency is low due to the fact that manual intervention is needed in the sample injection process of an existing mass spectrometer is solved, and the technical effects that automatic sample injection control is achieved through automatic sample recognition and intelligent parameter matching, and the overall mass spectrometry efficiency is improved are achieved.

The present subject matter is related to identification / extraction and quantification of mass separated chromatographic peaks obtained from chromatography coupled mass spectrometry instruments This includes both liquid and gas chromatography as well as high resolution and triple quadrupole type mass analyzers. This also includes the case when these are operated in conjunction with ion mobility separation devices. The raw data generated by the instrument is first converted into an open source format such as the mzML format, obvious noise is removed by applying ad hoc filters, mass scans are centroided, regions of interest (ROI) are built in Retention Time dimension, ROIs are binned into 3 or more buckets, longer ROIs are broken into special smaller ROIs using overlapping and sliding window approach, ROIs are classified as peak or noise, peak boundaries identified if classified as peak, longer ROIs formed from special smaller ROIs, and chromatography coupled mass spectrometry data Features are quantified and reported in Retention Time dimension. The ROI classification and peak boundary detection steps make use of specially trained deep learning methods.

Methods and systems are disclosed for predictive classification of a raw mass spectrum of a sample, the spectrum resulting from mass spectrometry analysis. The predictive classification is performed by a trained large-scale spectral model, including a self-supervised large-scale base spectral model trained with unidentified sample spectra from a plurality of sources and fine-tuned with a task-specific model trained with identified spectra.

The present invention determines how to extract data for a peptide precursor and its fragments in an efficient manner when processing the so-called diagonal family of dia- PASEF acquisition methods, such as synchro-PASEF or midia-PASEF. In one implementation of the invention, the entire ion cloud is treated as if it were acquired by a single diagonal DIA window.

Disclosed are systems and methods for facilitating compound identification based on analytical data obtained from an analytical instrument such as, for example, a mass spectrometer.

A method of performing liquid chromatography (LC) with an LC system including an LC column and an analyzer includes delivering a transport liquid to an open port interface (OPI) of a sample receiving circuit, wherein the sample receiving circuit includes a sample transfer chamber. A sample is ejected from a sample holder into the OPI with a non-contact ejector. The transport liquid and the sample are received in the sample transfer chamber. The sample transfer chamber is decoupled from the sample receiving circuit. A solvent is delivered to the analysis circuit. The sample is pushed from the sample transfer chamber with the solvent. The sample and the solvent is passed through the LC column to produce an eluent. The eluent is analyzed with the analyzer.

A micro-fabricated device (400A) for controlling trapped ions includes a first ion trap module (2A) including first electrodes configured to trap ions in a zone above the first electrodes and a second ion trap module laterally arranged next to the first ion trap module (2B, 2C) and including second electrodes configured to trap ions in a zone above the second electrodes. The first ion trap module and the second ion trap module are mechanically connected. The first electrodes and the second electrodes are aligned relative to each other so as to be configured to provide a shuttling of ions between the first ion trap module and the second ion trap module along the first electrodes and the second electrodes.

An example method performed via a computing device for providing support for a mass spectrometry (MS) system includes obtaining from a mass spectrometry library (i) a ranked hit list of reference spectra corresponding to a set of fragmented spectra of an analyte acquired with the MS system; and (ii) a first set of metadata corresponding to the list of sorted hit items for the reference spectrum. The method also includes obtaining, from the MS system, a second set of metadata corresponding to the set of fragmented spectra. The method further includes evaluating an order of entries in the ranked hit item list with a machine learning model based on the first set of metadata and the second set of metadata.

Systems, methods, and cartridges taught herein improve chromatographic performance in electrospray systems that feature chromatographic columns having a conductive or semiconductive stationary phase by electrically connecting a fluid junction located upstream of the chromatographic column to a fluid union located downstream of the chromatographic column using an electrical conductor. The electrical conductor creates a voltage equipotential between a first end of the chromatographic column and a second end of the chromatographic column that neutralizes current flow through the chromatographic column. Accurate electrospray current measurements are enabled while post-column peak dispersion and repeatable retention time are achieved.

A method for collaborative source apportionment of volatile organic compounds (VOCs), a product, a medium, and a device are provided. The method includes: establishing a single particle classification model; training and optimizing the single particle classification model with a local pollution library, and analyzing pollution sources of single particle mass spectrometric data to be apportioned to obtain a time series of the pollution sources contributing to the particulate matter (PM); obtaining VOCs factors of the pollution sources and a time series thereof; performing correlation calculation on the time series of the pollution sources contributing to the PM and the time series of the VOCs factors to obtain a correlation coefficient; and attributing the PM and the VOCs factor having the correlation coefficient higher than a set threshold to a same pollution source, and identifying a common pollution source of the PM and the VOCs.

Systems and methods are provided for obtaining raw mass spectrometry data from samples, extracting a subset of the raw mass spectrometry data based on storage characteristics of the computing system, storing the subset of the raw mass spectrometry data within a storage of the computing system, and transmitting the stored subset to a machine learning component.

A method of analysis using mass and / or ion mobility spectrometry or ion mobility spectrometry is disclosed comprising: using a first device to generate aerosol, smoke or vapour from one or more regions of a first target of biological material; and mass and / or ion mobility analysing and / or ion mobility analysing said aerosol, smoke, or vapour, or ions derived therefrom so as to obtain first spectrometric data. The method may use an ambient ionisation method.

In this composition estimation method, first data that includes a change in mass of a sample and second data that includes the mass spectrum of the gas constituents of a sample are acquired. The second data is broken down into the product of a base spectrum matrix and an intensity distribution matrix by non-negative matrix factorization; a second intensity distribution matrix is obtained by correcting the effects of differences in ionization efficiency for each gas constituent reflected in the intensity distribution matrix on the basis of the first data to produce a mass reference; the second intensity distribution matrix is subjected to non-negative matrix factorization and broken down into the product of a matrix that includes mass proportions in each sample of a virtual sample and a matrix that includes characteristic vectors of the virtual sample; a (k − 1)-dimensional simplex is established that encompasses all of the characteristic vectors and has characteristic vectors as end members; and the content ratio of each constituent element in the sample is estimated on the basis of the distance ratios between K end members and the characteristic vectors of the sample. According to this method, the problem of the accuracy and / or the reproducibility inherent in conventional methods can be improved.

The method for non-targeted mass spectrometric identification of tobacco carbonyl components includes the steps of: (1) preparing a tobacco sample extract; (2) derivatizing the extract with 2,4-dinitrophenylhydrazine (DNPH) to obtain a first tobacco sample; (3) derivatizing the extract with DNPH-d3 instead of DNPH to obtain a second tobacco sample; (4) preparing a mixed tobacco sample; (5) preparing a blank sample; (6) performing UPLC-Orbitrap-HRMS analysis on each of the first tobacco sample, the second tobacco sample, the mixed tobacco sample, and the blank sample to obtain LC-MS raw data; (7) processing the LC-MS raw data to obtain raw mass spectrometry characteristic data; (8) performing multistage filtering on the raw mass spectrometry characteristic data; and (9) performing structural annotation or identification on the finally retained chromatographic peaks to obtain tobacco carbonyl components. By performing multi-stage filtering on the mass spectrometry profile data, the chromatographic-mass spectrometry information of noise or interference components in the raw data set can be rapidly removed to obtain compositional information of aldehyde and ketone chemical components in cigarettes.

A method of determining a confidence measure of precursor inference in mass spectrum data. The method includes evaluating the response profile using response profile metrics and reporting the confidence measure of the precursor inference using the response profile and the evaluating of the response profile. Also a method of detecting conflicting relationships between precursors including filtering an intensity at at least one position across the scanning ion transmission window dimension. Another method a method of detecting conflicting relationships between precursors includes generating, based on an encoding, a trace of the scanning ion transmission window dimension wherein each sum is substituted with a binary metric for presence of the at least one unique product ion in each window. Further a method of identifying a precursor m / z including determining an intercept between the rising linear function and the falling linear function, and identifying the precursor m / z using the intercept.

A measured mass spectrum and intensity data provided as a function of m / z and at least one additional dimension are received. Peaks of the measured spectrum are compared to peaks of each of a plurality of library mass spectra. A set of library mass spectra is identified using a fit score. For each spectrum of the set, a group of related peaks of the measured spectrum calculated using a deconvolution algorithm is recalculated. The recalculation lowers a threshold for selection in the group if a matching peak of the library spectrum contributed to the fit score. A group of related peaks of the measured spectrum is produced for each library spectrum. For each spectrum of the set, peaks of the group are compared to peaks of the library spectrum and a purity score is calculated. At least one library spectrum of the set with the highest purity score is identified.

A method of performing mass spectrometry includes obtaining, based on a series of mass spectra of detected ions derived from components eluting from a chromatography column, a plurality of extracted ion chromatograms (XICs), each XIC comprising a plurality of detection points representing detected intensity for a distinct selected m / z as a function of time; detecting, based on the series of mass spectra, precursor ions of each distinct selected m / z of the plurality of XICs; and determining, for each XIC based on a set of detection points of the XIC, a predicted next detection point to be obtained based on a next mass spectrum to be acquired.

An apparatus for manipulation of a gaseous ion beam advantageously uses an ultrasonic acoustic field rather than physical structures, which can become contaminated or electromagnetic fields, which require high field strengths. Such apparatuses comprise an ion source having an outlet configured to release the gaseous ion beam at a flow rate along a first path; a transducer configured to generate an acoustic field along a second path that intersects or overlaps with the first path; and an ion detector. Aspects of the disclosure also provide methods of using the same to deflect, gate, focus, and separate gaseous ion beams.

The disclosure concerns a method of analysing a sample comprising an intact membrane protein, in a sample comprising non-synthetic biological membrane material. The method involves irradiating a gas phase proto membrane protein ion with infrared irradiation from a laser source.

An ion trap (1) is specified, with - a set of electrodes (2) designed to enclose and / or manipulate at least one ion in a processing area (5), - at least one magnetic arrangement (3) configured to generate a magnetic field in the processing area (5), and - at least two magnetic field sensor devices (4) spaced apart from the processing area (5), wherein - the at least two magnetic field sensor devices (4) are arranged inside the ion trap (1), and - the at least two magnetic field sensor devices (4) are designed to provide characteristic distribution information for the magnetic field in the processing area (5). Furthermore, a method for adjusting a magnetic field is described.

Aspects of the present disclosure relate generally to systems and methods for use in the implementation and / or operation of quantum information processing (QIP) systems, and more particularly, to a technique for the fabrication of laser-ablation targets used in atomic sources for QIP systems that are based on atomic-based qubits.