Patent Description:
Multicollector mass spectrometers, such as inductively coupled plasma mass spectrometers (MC-ICP-MS), are instruments used to investigate small differences in the abundance ratios of analysed isotopes.

For example, Strontium (Sr) has <NUM> isotopes with the following masses and abundances:.

With improvements in the accuracy of mass spectrometers during the last century, it became clear that the isotope ratios (87Sr/86Sr ≈ <NUM> or 84Sr/87Sr ≈ <NUM>) are not identical across different samples. Furthermore, it became apparent that the accurate determination of Sr isotope ratios is a powerful tool for archaeologists. Since the isotopic composition of the diet of an individual is preserved in the bones of an individual, a statement about the place of birth of an individual can be made when matching the Sr isotope ratios of the bones of a dead body to the Sr isotope ratios of the soil in a specific region.

Besides Sr, there are many other isotope systems which are of interest for scientific or technical questions. Another example is the Rubidium - Strontium (Rb - Sr) dating which makes use of the fact that 87Rb decays into 87Sr with a half-life of about <NUM> billion years: by determining the 87Sr/86Sr ratio as well as the Rb/Sr ratio of different minerals of a sample, the time elapsed since the sample crystallised can be calculated.

However, the 87Sr cannot be easily mass resolved from the 87Rb (<NUM> amu to <NUM> amu would require a mass resolving power of more than <NUM>,<NUM>, while the upper limit of commercially available isotope ratio mass spectrometers is below <NUM>,<NUM>). Isotopic methods that suffer from isotopic interferences as the Rb/Sr method therefore require complex chemical cleaning steps prior to the actual measurement with a mass spectrometer, which makes these methods time consuming and limits them to samples that are available in relatively high quantities.

A solution for this problem is the use of a collision reaction cell: the ions are guided through a cell which is filled with a reactive gas. With an appropriate choice of the gas, one can obtain that the analyte ions are mass-shifted (by forming molecules when reacting with the gas), while the interfering ions are not. For example, the analyte ions become <NUM> atomic mass units (amu) heavier when reacting with oxygen, while the mass of the interfering ions remains the same.

By doing this, the mass difference of sample ions and interfering ions, which was marginal before entering the collision cell, becomes large enough to be easily resolved by the mass spectrometer downstream of the collision cell.

To avoid the problem of elements that interfere with the mass shifted analyte ions, a pre-mass-filter with a bandpass characteristic can be used, such that only the masses of interest reach the collision cell while ions that interfere with the mass shifted ions are not transmitted. In <CIT>, a pre-filter comprising a combination of two Wien filters is disclosed. Such a pre-filter can have a mass-independent transmission.

A pre-filter comprising a combination of two Wien filters is suitable for blocking the intense Ar beam caused by the plasma source of ICP-MS instruments as early as possible in the ion optics. By blocking the Ar beam, the total ion load of the ion beam is greatly reduced. This is beneficial for the resolving power of the instrument and, in particular, for the abundance sensitivity. However, optimising the settings of a pre-filter comprising a double Wien filter is not intuitive.

<CIT> describes, according to its abstract, a method and mass spectrometer for filtering ions. The mass spectrometer generally comprises an ion guide, a quadrupole mass filter, a collision cell and a time of flight (ToF) detector, and is enabled to transmit an ion beam through to the ToF detector. The mass spectrometer is operated in MS mode, such that ions in the ion beam remain substantially unfragmented, the quadrupole mass filter operating at a pressure substantially lower than in either of the ion guide and the collision cell. The quadrupole mass filter is operated in a bandpass mode such that ions outside of a range of interest are filtered from the ion beam, leaving ions inside the range of interest in the ion beam. The ions inside the range of interest are analyzed at the ToF detector.

<CIT> describes, according to its abstract, an isotope ratio mass spectrometer having an ion source, a static field mass filter, a reaction cell to induce a mass shift reaction, and a sector field mass analyser for spatially separating ions from the reaction cell according to their m/z. A detector platform detects a plurality of different ion species separated by the sector field mass analyser. The static field mass filter has a first Wien filter that deflects ions away from a longitudinal symmetry axis of the spectrometer in accordance with the ions' m/z, and a second Wien filter that deflects ions back towards the longitudinal symmetry axis in accordance with the ions' m/z. An inverting lens is positioned along the longitudinal axis between the Wien filters to invert the direction of deflection of the ions from the first Wien filter.

The invention to which the present European patent relates is defined in the accompanying independent claims.

A method of determining, for a target ion mass, a bandpass range of an ion filter for a mass spectrometer is disclosed herein. The method comprises:.

Optionally, the method further comprises, for each scanned mass range, determining a third mass associated with a third ion intensity corresponding with a third fraction of the respective maximum intensity, and a fourth mass associated with a fourth ion intensity corresponding with a fourth fraction of the respective maximum intensity, wherein the third mass is lower than the centre mass of the ion species and the fourth mass is higher than the centre mass of the ion species; and
for the target ion mass having a known centre mass, deriving the masses having the associated third ion intensity and fourth ion intensity from the determined third ion intensities and fourth ion intensities of the scanned masses.

Optionally, the masses of the at least two ion species are evenly distributed over a mass range of interest.

Optionally, the method further comprises displaying one or more of the interpolated masses and, optionally, one or more of the determined masses.

Optionally, the method further comprises displaying the transmission windows of the interpolated expected masses.

A method of determining an expected response to injecting a beam of ions of a species of interest into a static field mass filter of a mass spectrometer comprising a mass analyser is disclosed herein. The method comprises:.

Optionally, at least one of the first and second interpolant functions is a polynomial function.

Optionally, the method further comprises, for each species of the plurality of test ion species and for each of the plurality of test magnetic field strengths:.

Optionally, the method further comprises, for each species of the plurality of test ion species and for each of the plurality of test magnetic field strengths:
based on the first predetermined relationship between magnetic field strength, electric field strength and centre mass of the static field mass filter, determining a second lower mass corresponding to the fourth electric field strength and the test magnetic field strength and determining a second higher mass corresponding to the fifth electric field strength and the test magnetic field strength.

Optionally, the static field mass filter comprises a first Wien filter and a second Wien filter.

Optionally, each species of the plurality of test ion species has a mass and wherein the masses of the plurality of test ion species are evenly distributed over a mass range of interest.

Optionally, the method is computer-implemented.

There is disclosed an apparatus configured to perform any of the methods described herein.

There is disclosed a computer-readable medium comprising instructions which, when executed by a processor of an apparatus, cause the apparatus to perform any of the methods described herein.

Examples of the present disclosure will now be explained with reference to the accompanying drawings in which:.

Throughout the description and the drawings, like reference numerals refer to like parts. Implementations are illustrated by way of example, not by way of limitation, in the figures of the accompanying drawings.

In overview, a method of determining an expected response to injecting a beam of ions of at least one species of interest into a static field mass filter of a mass spectrometer comprising a mass analyser is disclosed herein.

The method comprises: measuring an intensity of ions injected into the static field mass filter for various combinations of test ion species, test magnetic field strengths, and test electric field strengths; determining electric field strengths corresponding to an intensity equal to a first and a second fraction of the measured peak intensity; based on a predetermined relationship between magnetic field strength, electric field strength and centre mass of the static field mass filter, determining mass values corresponding to the determined electric field strengths; and interpolating, from said mass values and for each ion species and for at least one of the test magnetic field strengths, expected mass values for an ion species of interest.

The present approach is for use in a mass spectrometer including a static field mass filter such as (but not limited to) that described in <CIT> (e.g., Thermo Fisher Scientific's "Neoma™ MS/MS MC-ICP-MS"). Static field mass filters may apply a constant electric field and apply a magnetic field. This leads to a flat transmission of ions across a selected mass-to-charge ratio (m/z) range, and small deviations in system tuning should not change the measured isotope ratio in an unpredictable way. The static field mass filter is able to select a mass window prior to entry of the ions into a reaction cell. Although masses are separated by static magnetic and electric fields, the complete arrangement of the ion optical pre-filter setup does not introduce a lateral mass discrimination for the selected m/z window at the relatively small input aperture of a reaction cell.

Preferably, the static field mass filter comprises a first and a second Wien filter with an inversion lens between them. This arrangement uses static and not time-dependent (RF-based) ion optics to separate the ions and, as a result of the symmetry between the first and second Wien filters and the use of an inversion lens, mass-to-charge separation introduced within the static field mass filter is nullified at the exit thereof. The resulting instrument may be tuned along the path of the ions, because there is a relatively simple relationship between the electric and magnetic fields, and the mass-to-charge ratio of the ions.

The design of a double Wien-filter preceding the standard Neoma™ mass spectrometer in the Neoma™ MS/MS MC-ICP-MS provides a pre-filter which allows unwanted ion beams, such as Argon (Ar), to be cut from the mass spectrum or to clean a mass range for reacting analytes into this mass area for online chemical separation via a collision cell. Tuning this part of the instrument is important for good performance.

In the case of Neoma™ MS/MS a combination of two Wien filters has been chosen for pre-filtering the ions. This is because, to avoid the problem of elements that interfere with the mass shifted analyte ions, a pre-mass-filter with a bandpass characteristic should be used such that only the masses of interest reach the collision cell while ions that interfere with the mass shifted ions are not transmitted. The benefit of using two Wien filters is that it does not require alternating potentials (as in a quadrupole filter) which usually lead to a mass dependent transmission.

Another important function of the pre-filter is to block the intense Ar beam that is apparent in mass spectrometers with a plasma source as early as possible in the ion optics. By blocking the Ar beam the total ion load of the ion beam is greatly reduced, which is beneficial for the resolving power of the instrument and most of all beneficial for the abundance sensitivity.

Optimising the settings of the double Wien filter that is used in the Neoma™ MS/MS for best analytical conditions specific for each isotope system to be analysed is not intuitive for the operator since the parameters that control the transmission behaviour of the pre-filter have overlapping effects on the width of the bandpass, the steepness of its flanks and on the centre mass. A second reason why the tuning is not intuitive is that the flanks of the transmission curve are relatively wide and not symmetrical.

To predict the transmission of the double Wien filter a calibration is useful. Having a set procedure to perform the calibration prevents a trial and error experience for the user when trying to obtain a bandpass characteristic of the pre-filter for tuning the MS/MS Wien pre-filter settings.

It is noted that the present disclosure is not limited to mass spectrometers having an ICP ion source but can also be applied to mass spectrometers having an electron ionization ion source, a chemical ionization ion source, an electrospray ion source, a matrix-assisted laser desorption/ionization (MALDI) ion source, a photoionization ion source, a glow discharge ionization sources, a thermal ionization source and/or any other suitable ionization source.

The approaches described herein may be implemented using the apparatus or system(s) described below.

<FIG> is a block diagram of a scientific instrument support module <NUM> for performing support operations, in accordance with various implementations. The scientific instrument support module <NUM> may be implemented by circuitry (e.g., including electrical and/or optical components), such as a programmed computing device. The logic of the scientific instrument support module <NUM> may be included in a single computing device, or may be distributed across multiple computing devices that are in communication with each other as appropriate. Examples of computing devices that may, singly or in combination, implement the scientific instrument support module <NUM> are discussed herein with reference to the computing device <NUM> of <FIG>. Examples of systems of interconnected computing devices, in which the scientific instrument support module <NUM> may be implemented across one or more of the computing devices, are discussed herein with reference to the scientific instrument support system <NUM> of <FIG>.

The scientific instrument support module <NUM> may include first logic <NUM>, second logic <NUM>, and third logic <NUM>. As used herein, the term "logic" may include an apparatus that is to perform a set of operations associated with the logic. For example, any of the logic elements included in the support module <NUM> may be implemented by one or more computing devices programmed with instructions to cause one or more processing devices of the computing devices to perform the associated set of operations. In a particular implementation, a logic element may include one or more non-transitory computer-readable media having instructions thereon that, when executed by one or more processing devices of one or more computing devices, cause the one or more computing devices to perform the associated set of operations. As used herein, the term "module" may refer to a collection of one or more logic elements that, together, perform a function associated with the module. Different ones of the logic elements in a module may take the same form or may take different forms. For example, some logic in a module may be implemented by a programmed general-purpose processing device, while other logic in a module may be implemented by an application-specific integrated circuit (ASIC). In another example, different ones of the logic elements in a module may be associated with different sets of instructions executed by one or more processing devices. A module may not include all of the logic elements depicted in the associated drawing; for example, a module may include a subset of the logic elements depicted in the associated drawing when that module is to perform a subset of the operations discussed herein with reference to that module.

As mentioned above, the scientific instrument support module <NUM> may be implemented in a system of interconnected computing devices. In such a system, the scientific instrument support module <NUM> may interact with a scientific instrument <NUM> (the interaction with which is discussed herein with reference to <FIG>) which may include any appropriate scientific instrument, such as a mass spectrometer <NUM> having a static field mass filter <NUM>.

<FIG> shows a view of an example mass spectrometer <NUM> having a static field mass filter <NUM>.

The mass spectrometer <NUM> includes an ion source <NUM>. The ion source <NUM> includes a triaxial ICP torch <NUM>, a sampler cone <NUM>, one or more skimmer cones <NUM>, an extraction lens <NUM> and/or a further skimmer cone <NUM> and/or another ion optical device <NUM>. This results in a collimated ion beam.

Downstream of the ion source <NUM>, instead of a quadrupole (RF) mass filter, is positioned a static field mass filter <NUM> which will be described in further detail below. The static field mass filter <NUM> maintains constant electric and magnetic fields, so that transmission of ions through the static field mass filter has a flat response across the selected m/z range. A quadrupole mass filter does not provide such a flat response. This is because the ions are only influenced by static fields. In a quadrupole mass filter, the electromagnetic fields change with time according to the applied frequency. This results in a zig zag trajectory of the ions which are pushed back and forth. Moreover, small deviations in system tuning of the static field mass filter <NUM> do not change the measured isotope ratio in an unpredictable way. Nevertheless, the static field mass filter <NUM> does not introduce a lateral mass discrimination (as would happen in, for example, a magnetic sector analyser) so that the ion beam exiting the static field mass filter <NUM> can be focussed onto the relatively small (c. <NUM>) entrance aperture of a collision cell <NUM>, across the width of the mass window selected for transmission by the static field mass filter <NUM>.

Following the collision cell <NUM>, ions are accelerated by an accelerator <NUM> and focussed into the ion optics of a double focusing high resolution multicollector mass spectrometer for simultaneous detection of different isotopes (of the sample or standards). Further, the double focusing high resolution multicollector mass spectrometer again includes an electrostatic sector <NUM> and a magnetostatic sector <NUM>, separated by a focussing lens <NUM>. Downstream of the high resolution multicollector mass spectrometer, the arrangement contains dispersion optics <NUM> and finally a detector platform <NUM> again, for example, such as that described in <CIT>.

The preferred arrangement of a static field mass filter <NUM> in the arrangement of <FIG> is a double Wien filter. Wien filters employ an arrangement of crossed electrostatic and magnetostatic fields. Ions passing through this arrangement are subject to the magnetic Lorentz force and the electric field strength.

<FIG> is simplified diagram of a pre-filter chamber <NUM> of a mass spectrometer <NUM> having a static field mass filter <NUM> in which some or all of the methods disclosed herein may be performed, in accordance with various implementations.

The mass spectrometer also has a collision cell chamber <NUM> and an extraction area <NUM>, respectively downstream and upstream of the pre-filter chamber <NUM>.

The mass spectrometer <NUM> may also include a mass analyser (not shown) downstream of the collision cell chamber <NUM> shown in <FIG>.

The pre-filter chamber <NUM> of the mass spectrometer <NUM> may comprise an entrance aperture <NUM>, a static field mass filter <NUM>, and an exit aperture <NUM>. The static field mass filter <NUM> may comprise a first lens (or 'lens one') <NUM>, a first Wien filter <NUM>, a slit <NUM> (which can be narrowed to a minimum slit width and widened to a width larger than the minimum slit width), a second lens (or 'lens two') <NUM>, a second Wien filter <NUM>, and a third lens (or 'lens three') <NUM>.

The collision cell chamber <NUM> may comprise a fourth lens (or 'lens four') <NUM> and a collision cell <NUM>.

The extraction area <NUM> may comprise an extraction lens (not shown) for injecting a beam of ions <NUM> comprising one or more ion species into the pre-filter chamber <NUM> through entrance aperture <NUM>.

The beam <NUM> may pass through the pre-filter chamber <NUM> and exit from exit aperture <NUM> to yield beam <NUM>. The beam <NUM> exiting the pre-filter chamber <NUM> may enter the collision cell chamber <NUM>. In the collision cell chamber <NUM>, exiting beam <NUM> may pass through fourth lens <NUM> and collision cell <NUM> to yield beam <NUM>.

The methods disclosed herein may include interactions with a human user (e.g., via the user local computing device <NUM> discussed herein with reference to <FIG>). These interactions may include providing information to the user (e.g., information regarding the operation of a scientific instrument such as the scientific instrument <NUM> of <FIG>, information regarding a sample being analysed or other test or measurement performed by a scientific instrument, information retrieved from a local or remote database, or other information) or providing an option for a user to input commands (e.g., to control the operation of a scientific instrument such as the scientific instrument <NUM> of <FIG>, or to control the analysis of data generated by a scientific instrument), queries (e.g., to a local or remote database), or other information. In some implementations, these interactions may be performed through a graphical user interface (GUI) that includes a visual display on a display device (e.g., the display device <NUM> discussed herein with reference to <FIG>) that provides outputs to the user and/or prompts the user to provide inputs (e.g., via one or more input devices, such as a keyboard, mouse, trackpad, or touchscreen, included in the other I/O devices <NUM> discussed herein with reference to <FIG>). The scientific instrument support systems disclosed herein may include any suitable GUIs for interaction with a user.

<FIG> depicts an example GUI <NUM> that may be used in the performance of some or all of the methods disclosed herein, in accordance with various implementations. As noted above, the GUI <NUM> may be provided on a display device (e.g., the display device <NUM> discussed herein with reference to <FIG>) of a computing device (e.g., the computing device <NUM> discussed herein with reference to <FIG>) of a scientific instrument support system (e.g., the scientific instrument support system <NUM> discussed herein with reference to <FIG>), and a user may interact with the GUI <NUM> using any suitable input device (e.g., any of the input devices included in the other I/O devices <NUM> discussed herein with reference to <FIG>) and input technique (e.g., movement of a cursor, motion capture, facial recognition, gesture detection, voice recognition, actuation of buttons, etc.).

The GUI <NUM> may include a data display region <NUM>, a data analysis region <NUM>, a scientific instrument control region <NUM>, and a settings region <NUM>. The particular number and arrangement of regions depicted in <FIG> is simply illustrative, and any number and arrangement of regions, including any desired features, may be included in a GUI <NUM>.

The data display region <NUM> may display data generated by a scientific instrument (e.g., the scientific instrument <NUM> discussed herein with reference to <FIG>). For example, the data display region <NUM> may display any intensities, magnetic and/or electric field strengths, test species, and interpolant functions according to the methods disclosed herein.

The data analysis region <NUM> may display the results of data analysis (e.g., the results of analysing the data illustrated in the data display region <NUM> and/or other data). For example, the data analysis region <NUM> may display determined masses, expected masses and/or any other result of an interpolation (e.g., a plot), as determined in the approaches described herein. In some implementations, the data display region <NUM> and the data analysis region <NUM> may be combined in the GUI <NUM> (e.g., to include data output from a scientific instrument, and some analysis of the data, in a common graph or region).

The scientific instrument control region <NUM> may include options that allow the user to control a scientific instrument (e.g., the scientific instrument <NUM> discussed herein with reference to <FIG>). For example, the scientific instrument control region <NUM> may include controls to cause a beam of ions to be injected into the static field mass filter <NUM> or to measure an intensity of ions of the species in a beam.

The settings region <NUM> may include options that allow the user to control the features and functions of the GUI <NUM> (and/or other GUIs) and/or perform common computing operations with respect to the data display region <NUM> and data analysis region <NUM> (e.g., saving data on a storage device, such as the storage device <NUM> discussed herein with reference to <FIG>, sending data to another user, labelling data, etc.). For example, the settings region <NUM> may include settings to switch between automated and manual analysis modes of the mass spectrometer <NUM>.

As noted above, the scientific instrument support module <NUM> may be implemented by one or more computing devices. <FIG> is a block diagram of a computing device <NUM> that may perform some or all of the methods disclosed herein, in accordance with various implementations. In some implementations, the scientific instrument support module <NUM> may be implemented by a single computing device <NUM> or by multiple computing devices <NUM>. Further, as discussed below, a computing device <NUM> (or multiple computing devices <NUM>) that implements the scientific instrument support module <NUM> may be part of one or more of the scientific instrument <NUM>, the user local computing device <NUM>, the service local computing device <NUM>, or the remote computing device <NUM> of <FIG>.

The computing device <NUM> of <FIG> is illustrated as having a number of components, but any one or more of these components may be omitted or duplicated, as suitable for the application and setting. In some implementations, some or all of the components included in the computing device <NUM> may be attached to one or more motherboards and enclosed in a housing (e.g., including plastic, metal, and/or other materials). In some implementations, some these components may be fabricated onto a single system-on-a-chip (SoC) (e.g., an SoC may include one or more processing devices <NUM> and one or more storage devices <NUM>). Additionally, in various implementations, the computing device <NUM> may not include one or more of the components illustrated in <FIG>, but may include interface circuitry (not shown) for coupling to the one or more components using any suitable interface (e.g., a Universal Serial Bus (USB) interface, a High-Definition Multimedia Interface (HDMI) interface, a Controller Area Network (CAN) interface, a Serial Peripheral Interface (SPI) interface, an Ethernet interface, a wireless interface, or any other appropriate interface). For example, the computing device <NUM> may not include a display device <NUM>, but may include display device interface circuitry (e.g., a connector and driver circuitry) to which a display device <NUM> may be coupled.

The computing device <NUM> may include a processing device <NUM> (e.g., one or more processing devices). As used herein, the term "processing device" may refer to any device or portion of a device that processes electronic data from registers and/or memory to transform that electronic data into other electronic data that may be stored in registers and/or memory. The processing device <NUM> may include one or more digital signal processors (DSPs), application-specific integrated circuits (ASICs), central processing units (CPUs), graphics processing units (GPUs), cryptoprocessors (specialized processors that execute cryptographic algorithms within hardware), server processors, or any other suitable processing devices.

The computing device <NUM> may include a storage device <NUM> (e.g., one or more storage devices). The storage device <NUM> may include one or more memory devices such as random access memory (RAM) (e.g., static RAM (SRAM) devices, magnetic RAM (MRAM) devices, dynamic RAM (DRAM) devices, resistive RAM (RRAM) devices, or conductive-bridging RAM (CBRAM) devices), hard drive-based memory devices, solid-state memory devices, networked drives, cloud drives, or any combination of memory devices. In some implementations, the storage device <NUM> may include memory that shares a die with a processing device <NUM>. In such an implementation, the memory may be used as cache memory and may include embedded dynamic random access memory (eDRAM) or spin transfer torque magnetic random access memory (STT-MRAM), for example. In some implementations, the storage device <NUM> may include computer readable media having instructions thereon that, when executed by one or more processing devices (e.g., the processing device <NUM>), cause the computing device <NUM> to perform any appropriate ones of or portions of the methods disclosed herein. The computer-readable media may be transitory (e.g., a wire or a wireless propagation medium in which a signal is being transmitted) or non-transitory.

The computing device <NUM> may include an interface device <NUM> (e.g., one or more interface devices <NUM>). The interface device <NUM> may include one or more communication chips, connectors, and/or other hardware and software to govern communications between the computing device <NUM> and other computing devices. For example, the interface device <NUM> may include circuitry for managing wireless communications for the transfer of data to and from the computing device <NUM>. The term "wireless" and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communications channels, etc., that may communicate data through the use of modulated electromagnetic radiation through a nonsolid medium. The term does not imply that the associated devices do not contain any wires, although in some implementations they might not. Circuitry included in the interface device <NUM> for managing wireless communications may implement any of a number of wireless standards or protocols, including but not limited to Institute for Electrical and Electronic Engineers (IEEE) standards including Wi-Fi (IEEE <NUM> family), IEEE <NUM> standards (e.g., IEEE <NUM>-<NUM> Amendment), Long-Term Evolution (LTE) project along with any amendments, updates, and/or revisions (e.g., advanced LTE project, ultramobile broadband (UMB) project (also referred to as "3GPP2"), etc.). In some implementations, circuitry included in the interface device <NUM> for managing wireless communications may operate in accordance with a Global System for Mobile Communication (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Evolved HSPA (E-HSPA), or LTE network. In some implementations, circuitry included in the interface device <NUM> for managing wireless communications may operate in accordance with Enhanced Data for GSM Evolution (EDGE), GSM EDGE Radio Access Network (GERAN), Universal Terrestrial Radio Access Network (UTRAN), or Evolved UTRAN (E-UTRAN). In some implementations, circuitry included in the interface device <NUM> for managing wireless communications may operate in accordance with Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Digital Enhanced Cordless Telecommunications (DECT), Evolution-Data Optimized (EV-DO), and derivatives thereof, as well as any other wireless protocols that are designated as <NUM>, <NUM>, <NUM>, and beyond. In some implementations, the interface device <NUM> may include one or more antennas (e.g., one or more antenna arrays) for receipt and/or transmission of wireless communications.

In some implementations, the interface device <NUM> may include circuitry for managing wired communications, such as electrical, optical, or any other suitable communication protocols. For example, the interface device <NUM> may include circuitry to support communications in accordance with Ethernet technologies. In some implementations, the interface device <NUM> may support both wireless and wired communication, and/or may support multiple wired communication protocols and/or multiple wireless communication protocols. For example, a first set of circuitry of the interface device <NUM> may be dedicated to shorter-range wireless communications such as Wi-Fi or Bluetooth, and a second set of circuitry of the interface device <NUM> may be dedicated to longer-range wireless communications such as global positioning system (GPS), EDGE, GPRS, CDMA, WiMAX, LTE, EV-DO, or others. In some implementations, a first set of circuitry of the interface device <NUM> may be dedicated to wireless communications, and a second set of circuitry of the interface device <NUM> may be dedicated to wired communications.

The computing device <NUM> may include battery/power circuitry <NUM>. The battery/power circuitry <NUM> may include one or more energy storage devices (e.g., batteries or capacitors) and/or circuitry for coupling components of the computing device <NUM> to an energy source separate from the computing device <NUM> (e.g., AC line power).

The computing device <NUM> may include a display device <NUM> (e.g., multiple display devices). The display device <NUM> may include any visual indicators, such as a heads-up display, a computer monitor, a projector, a touchscreen display, a liquid crystal display (LCD), a light-emitting diode (LED) display, or a flat panel display.

The computing device <NUM> may include other input/output (I/O) devices <NUM>. The other I/O devices <NUM> may include one or more audio output devices (e.g., speakers, headsets, earbuds, alarms, etc.), one or more audio input devices (e.g., microphones or microphone arrays), location devices (e.g., GPS devices in communication with a satellite-based system to receive a location of the computing device <NUM>, as known in the art), audio codecs, video codecs, printers, sensors (e.g., thermocouples or other temperature sensors, humidity sensors, pressure sensors, vibration sensors, accelerometers, gyroscopes, etc.), image capture devices such as cameras, keyboards, cursor control devices such as a mouse, a stylus, a trackball, or a touchpad, bar code readers, Quick Response (QR) code readers, or radio frequency identification (RFID) readers, for example.

The computing device <NUM> may have any suitable form factor for its application and setting, such as a handheld or mobile computing device (e.g., a cell phone, a smart phone, a mobile internet device, a tablet computer, a laptop computer, a netbook computer, an ultrabook computer, a personal digital assistant (PDA), an ultramobile personal computer, etc.), a desktop computing device, or a server computing device or other networked computing component.

One or more computing devices implementing any of the scientific instrument support modules or methods disclosed herein may be part of a scientific instrument support system. <FIG> is a block diagram of an example scientific instrument support system <NUM> in which some or all of the methods disclosed herein may be performed, in accordance with various implementations. The scientific instrument support modules and methods disclosed herein (e.g., the scientific instrument support module <NUM> of <FIG> and the method <NUM> of <FIG> and <FIG>) may be implemented by one or more of the scientific instrument <NUM>, the user local computing device <NUM>, the service local computing device <NUM>, or the remote computing device <NUM> of the scientific instrument support system <NUM>.

Any of the scientific instrument <NUM>, the user local computing device <NUM>, the service local computing device <NUM>, or the remote computing device <NUM> may include any of the implementations of the computing device <NUM> discussed herein with reference to <FIG>, and any of the scientific instrument <NUM>, the user local computing device <NUM>, the service local computing device <NUM>, or the remote computing device <NUM> may take the form of any appropriate ones of the implementations of the computing device <NUM> discussed herein with reference to <FIG>.

The scientific instrument <NUM>, the user local computing device <NUM>, the service local computing device <NUM>, or the remote computing device <NUM> may each include a processing device <NUM>, a storage device <NUM>, and an interface device <NUM>. The processing device <NUM> may take any suitable form, including the form of any of the processing devices <NUM> discussed herein with reference to <FIG>, and the processing devices <NUM> included in different ones of the scientific instrument <NUM>, the user local computing device <NUM>, the service local computing device <NUM>, or the remote computing device <NUM> may take the same form or different forms. The storage device <NUM> may take any suitable form, including the form of any of the storage devices <NUM> discussed herein with reference to <FIG>, and the storage devices <NUM> included in different ones of the scientific instrument <NUM>, the user local computing device <NUM>, the service local computing device <NUM>, or the remote computing device <NUM> may take the same form or different forms. The interface device <NUM> may take any suitable form, including the form of any of the interface devices <NUM> discussed herein with reference to <FIG>, and the interface devices <NUM> included in different ones of the scientific instrument <NUM>, the user local computing device <NUM>, the service local computing device <NUM>, or the remote computing device <NUM> may take the same form or different forms.

The scientific instrument <NUM>, the user local computing device <NUM>, the service local computing device <NUM>, and the remote computing device <NUM> may be in communication with other elements of the scientific instrument support system <NUM> via communication pathways <NUM>. The communication pathways <NUM> may communicatively couple the interface devices <NUM> of different ones of the elements of the scientific instrument support system <NUM>, as shown, and may be wired or wireless communication pathways (e.g., in accordance with any of the communication techniques discussed herein with reference to the interface devices <NUM> of the computing device <NUM> of <FIG>). The particular scientific instrument support system <NUM> depicted in <FIG> includes communication pathways between each pair of the scientific instrument <NUM>, the user local computing device <NUM>, the service local computing device <NUM>, and the remote computing device <NUM>, but this "fully connected" implementation is simply illustrative, and in various implementations, various ones of the communication pathways <NUM> may be absent. For example, in some implementations, a service local computing device <NUM> may not have a direct communication pathway <NUM> between its interface device <NUM> and the interface device <NUM> of the scientific instrument <NUM>, but may instead communicate with the scientific instrument <NUM> via the communication pathway <NUM> between the service local computing device <NUM> and the user local computing device <NUM> and the communication pathway <NUM> between the user local computing device <NUM> and the scientific instrument <NUM>.

The scientific instrument <NUM> may include any appropriate scientific instrument, such as mass spectrometer <NUM> having a static field mass filter <NUM> and a mass analyser.

The user local computing device <NUM> may be a computing device (e.g., in accordance with any of the implementations of the computing device <NUM> discussed herein) that is local to a user of the scientific instrument <NUM>. In some implementations, the user local computing device <NUM> may also be local to the scientific instrument <NUM>, but this need not be the case; for example, a user local computing device <NUM> that is in a user's home or office may be remote from, but in communication with, the scientific instrument <NUM> so that the user may use the user local computing device <NUM> to control and/or access data from the scientific instrument <NUM>. In some implementations, the user local computing device <NUM> may be a laptop, smartphone, or tablet device. In some implementations the user local computing device <NUM> may be a portable computing device.

The service local computing device <NUM> may be a computing device (e.g., in accordance with any of the implementations of the computing device <NUM> discussed herein) that is local to an entity that services the scientific instrument <NUM>. For example, the service local computing device <NUM> may be local to a manufacturer of the scientific instrument <NUM> or to a third-party service company. In some implementations, the service local computing device <NUM> may communicate with the scientific instrument <NUM>, the user local computing device <NUM>, and/or the remote computing device <NUM> (e.g., via a direct communication pathway <NUM> or via multiple "indirect" communication pathways <NUM>, as discussed above) to receive data regarding the operation of the scientific instrument <NUM>, the user local computing device <NUM>, and/or the remote computing device <NUM> (e.g., the results of self-tests of the scientific instrument <NUM>, calibration coefficients used by the scientific instrument <NUM>, the measurements of sensors associated with the scientific instrument <NUM>, etc.). In some implementations, the service local computing device <NUM> may communicate with the scientific instrument <NUM>, the user local computing device <NUM>, and/or the remote computing device <NUM> (e.g., via a direct communication pathway <NUM> or via multiple "indirect" communication pathways <NUM>, as discussed above) to transmit data to the scientific instrument <NUM>, the user local computing device <NUM>, and/or the remote computing device <NUM> (e.g., to update programmed instructions, such as firmware, in the scientific instrument <NUM>, to initiate the performance of test or calibration sequences in the scientific instrument <NUM>, to update programmed instructions, such as software, in the user local computing device <NUM> or the remote computing device <NUM>, etc.). A user of the scientific instrument <NUM> may utilize the scientific instrument <NUM> or the user local computing device <NUM> to communicate with the service local computing device <NUM> to report a problem with the scientific instrument <NUM> or the user local computing device <NUM>, to request a visit from a technician to improve the operation of the scientific instrument <NUM>, to order consumables or replacement parts associated with the scientific instrument <NUM>, or for other purposes.

The remote computing device <NUM> may be a computing device (e.g., in accordance with any of the implementations of the computing device <NUM> discussed herein) that is remote from the scientific instrument <NUM> and/or from the user local computing device <NUM>. In some implementations, the remote computing device <NUM> may be included in a datacentre or other large-scale server environment. In some implementations, the remote computing device <NUM> may include network-attached storage (e.g., as part of the storage device <NUM>). The remote computing device <NUM> may store data generated by the scientific instrument <NUM>, perform analyses of the data generated by the scientific instrument <NUM> (e.g., in accordance with programmed instructions), facilitate communication between the user local computing device <NUM> and the scientific instrument <NUM>, and/or facilitate communication between the service local computing device <NUM> and the scientific instrument <NUM>.

In some implementations, one or more of the elements of the scientific instrument support system <NUM> illustrated in <FIG> may not be present. Further, in some implementations, multiple ones of various ones of the elements of the scientific instrument support system <NUM> of <FIG> may be present. For example, a scientific instrument support system <NUM> may include multiple user local computing devices <NUM> (e.g., different user local computing devices <NUM> associated with different users or in different locations). In another example, a scientific instrument support system <NUM> may include multiple scientific instruments <NUM>, all in communication with service local computing device <NUM> and/or a remote computing device <NUM>; in such an implementation, the service local computing device <NUM> may monitor these multiple scientific instruments <NUM>, and the service local computing device <NUM> may cause updates or other information to be "broadcast" to multiple scientific instruments <NUM> at the same time. Different ones of the scientific instruments <NUM> in a scientific instrument support system <NUM> may be located close to one another (e.g., in the same room) or farther from one another (e.g., on different floors of a building, in different buildings, in different cities, etc.). In some implementations, a scientific instrument <NUM> may be connected to an Internet-of-Things (IoT) stack that allows for command and control of the scientific instrument <NUM> through a web-based application, a virtual or augmented reality application, a mobile application, and/or a desktop application. Any of these applications may be accessed by a user operating the user local computing device <NUM> in communication with the scientific instrument <NUM> by the intervening remote computing device <NUM>. In some implementations, a scientific instrument <NUM> may be sold by the manufacturer along with one or more associated user local computing devices <NUM> as part of a local scientific instrument computing unit <NUM>.

In some implementations, different ones of the scientific instruments <NUM> included in a scientific instrument support system <NUM> may be different types of scientific instruments <NUM>. In some such implementations, the remote computing device <NUM> and/or the user local computing device <NUM> may combine data from different types of scientific instruments <NUM> included in a scientific instrument support system <NUM>.

A method <NUM> of determining an expected response to injecting a beam of ions of at least one species of interest into a static field mass filter <NUM> of a mass spectrometer <NUM> (or, in other words, a method of determining a bandpass range of a static field mass filter <NUM> of a mass spectrometer <NUM>) comprises steps <NUM> to <NUM> shown in <FIG> and <FIG> (or, alternatively, steps <NUM> to <NUM> shown in <FIG>). The mass spectrometer may comprise a mass analyser. The mass spectrometer may comprise a static field mass filter <NUM> which may comprise a first Wien filter <NUM> and a second Wien filter <NUM>. The steps shown in <FIG> and <FIG>, and the alternative steps shown in <FIG> are discussed together below.

Steps <NUM> to <NUM> of method <NUM> may be repeated (<NUM>) for each species 701A, 701B, 701C of a plurality of test ion species <NUM>. Each species 701A, 701B, 701C of the plurality of test ion species <NUM> has a mass <NUM>. The masses of the test ion species may be evenly distributed across a mass range of interest. The test ion species may be ion species that are different from the at least one ion species of interest.

At step <NUM>, a beam of ions of the test species 701A, 701B, 701C may be caused to be injected into the static field mass filter <NUM>. Alternatively, at step <NUM> a beam of ions at least two ion species (e.g., any two of 701A, 701B, 701C) may be caused to be injected into the static field mass filter <NUM>. The beam may, for example, be caused to be injected by sending a message to the mass spectrometer <NUM> to instruct the mass spectrometer <NUM> to inject a beam.

Steps <NUM> to <NUM> of method <NUM> may be repeated (<NUM>) for each magnetic field strength 702A, 702B, 702C, 702D of a plurality of test magnetic field strengths <NUM> of the static field mass filter <NUM>. The plurality of test magnetic field strengths may each be expressed as respective fractions of a predetermined maximum magnetic field strength (or 'reference value').

At step <NUM>, the magnetic field may be set to the test magnetic field strength 702A, 702B, 702C, or 702D.

Steps <NUM> to <NUM> of method <NUM> may be repeated (<NUM>) for each of a plurality of test electric field strengths of the static field mass filter <NUM>. The plurality of test electric field strengths may each be expressed as respective fractions of a predetermined maximum electric field strength (or 'reference value').

At step <NUM>, the electric field may be set to the test electric field strength.

As an alternative to steps <NUM> to <NUM>, at step <NUM>, a mass range of each of the ion species may be scanned by changing at least one parameter of the static field mass filter. Each ion species has a centre mass; a mass range of an ion species is a mass range including (or 'surrounding') the centre mass.

At step <NUM>, using the mass analyser, an intensity of ions of the species 701A, 701B, 701C in the beam may be measured for the test magnetic field strength 702A, 702B, 702C, or 702D and the test electric field strength. Alternatively, at steps 667a and 667b, an ion intensity associated with each scanned mass (e.g., the chosen two of 701A, 701B, 701C) may be determined using the mass spectrometer, and the ion intensities and the associated filter parameters of each scanned mass range may be registered (or 'recorded').

At step <NUM>, a first electric field strength of the plurality of test electric field strengths at which the measured intensity has reached a peak intensity may be determined. The associated peak intensity may also be determined. Alternatively, at step <NUM>, for each scanned mass range, a maximum ion intensity and the associated centre mass may be determined.

At step <NUM>, a second electric field strength <NUM> of the plurality of test electric field strengths at which the measured intensity has reached a first predetermined fraction of the peak intensity may be determined, the second electric field strength being higher than the first electric field strength. Alternatively, at step <NUM>, a first mass associated with a first measured ion intensity of a first species, corresponding with a first fraction of a maximum measured intensity, may be determined.

Step <NUM> in <FIG> indicates that the method <NUM> continues in <FIG>.

At step <NUM>, a third electric field strength <NUM> of the plurality of test electric field strengths at which the measured intensity has reached a second predetermined fraction of the peak intensity may be determined, the third electric field strength being lower than the first electric field strength. Alternatively, at step <NUM>, a second mass associated with a second measured ion intensity of the first species, corresponding with a second fraction of the maximum measured intensity, may be determined.

The first and second predetermined fractions are smaller than <NUM>. The first and second predetermined fractions may be the same. For example, the first and second predetermined fractions may be <NUM>.

At step <NUM>, based on a first predetermined relationship between magnetic field strength, electric field strength and centre mass of the static field mass filter <NUM>, a first lower mass <NUM> corresponding to the second electric field strength <NUM> and the test magnetic field strength 702A, 702B, 702C, or 702D may be determined and a first higher mass <NUM> corresponding to the third electric field strength <NUM> and the test magnetic field strength 702A, 702B, 702C, or 702D may be determined. The predetermined relationship is explained in further detail below. Alternatively, in steps <NUM> and <NUM>, the first mass may be lower than the centre mass of the first ion species and the second mass may be higher than the centre mass of the first ion species.

At step <NUM>, a first lower expected mass <NUM> for at least one ion species <NUM> of interest may be interpolated using a first interpolant function, from the first lower mass <NUM> for each test ion species 701A, 701B, 701C of the plurality of test ion species <NUM> and for at least one of the test magnetic field strengths 702A, 702B, 702C, or 702D.

At step <NUM>, a first higher expected mass <NUM> for the at least one ion species <NUM> of interest may be interpolated using a second interpolant function, from the first higher mass <NUM> for each 701A, 701B, 701C of the plurality of test ion species <NUM> and for the at least one of the test magnetic field strengths 702A, 702B, 702C, or 702D.

As an alternative to steps <NUM> to <NUM>, at step <NUM>, for a target ion mass, the masses having the associated first and second ion intensities may be derived from the determined first ion intensities and second ion intensities of the scanned masses.

One may wish to interpolate expected masses not only for an ion species <NUM> of interest but also, for example, for magnetic field strengths for which the method <NUM> has not been performed (e.g., different from test magnetic field strengths 702A, 702B, 702C, or 702D). This may be because it would be excessively time-consuming to take measurements for a large plurality of test magnetic field strengths <NUM> of the static field mass filter <NUM>.

To this end, as an addition to method <NUM>, a second lower expected mass for at least one magnetic field strength of interest may be interpolated using a third interpolant function, from the first lower mass <NUM> for each 702A, 702B, 702C, 702D of the plurality of test magnetic field strengths <NUM> and for at least one of the test ion species 701A, 701B, or 701C; and a second higher expected mass for the at least one magnetic field strength of interest may be interpolated using a fourth interpolant function, from the first higher mass <NUM> for each 702A, 702B, 702C, 702D of the plurality of test magnetic field strengths <NUM> and for the at least one of the test ion species 701A, 701B, or 701C.

Any of the first, second, third and fourth interpolant functions may be a polynomial function.

Method <NUM> may optionally comprise steps <NUM> and <NUM> shown in <FIG>. At step <NUM>, a first difference <NUM> between the first lower expected mass <NUM> and the first higher expected mass <NUM> may be determined. At step <NUM>, based on the first difference <NUM>, a passband width <NUM> of a bandpass characteristic <NUM> of the static field mass filter <NUM> may be determined for the at least one ion species of interest <NUM>.

One may wish to interpolate expected masses, in accordance with various implementations of method <NUM>, for other predetermined fractions of the peak intensity. This may be to determine a more detailed expected response of the mass spectrometer.

To this end, as an addition to method <NUM>, a fourth electric field strength <NUM> of the plurality of test electric field strengths at which the measured intensity has reached a third predetermined fraction of the peak intensity may be determined, the fourth electric field strength being higher than the first electric field strength; and a fifth electric field strength <NUM> of the plurality of test electric field strengths at which the measured intensity has reached a fourth predetermined fraction of the peak intensity may be determined, the fifth electric field strength being lower than the first electric field strength.

The third and fourth predetermined fractions are smaller than <NUM>. The third and fourth predetermined fractions may be the same. For example, the third and fourth predetermined fractions may be <NUM>. The third predetermined fraction may be smaller than the first predetermined fraction, and the fourth predetermined fraction may be smaller than the second predetermined fraction. For example, the first and second predetermined fractions may be <NUM>, and the third and fourth predetermined fractions may be <NUM>.

Additionally, based on the first predetermined relationship between magnetic field strength, electric field strength and centre mass of the static field mass filter <NUM>, a second lower mass <NUM> corresponding to the fourth electric field strength <NUM> and the test magnetic field strength 702A, 702B, 702C, or 702D may be determined and a second higher mass <NUM> corresponding to the fifth electric field strength <NUM> and the test magnetic field strength 702A, 702B, 702C, or 702D may be determined.

Additionally, a third lower expected mass <NUM> for at least one ion species <NUM> of interest may be interpolated using a fifth interpolant function, from the second lower mass <NUM> for each 701A, 701B, 701C of the plurality of test ion species <NUM> and for at least one of the test magnetic field strengths 702A, 702B, 702C, or 702D; and a third higher expected mass <NUM> for the at least one ion species <NUM> of interest may be interpolated using a sixth interpolant function, from the second higher mass <NUM> for each 701A, 701B, 701C of the plurality of test ion species <NUM> and for the at least one of the test magnetic field strengths 702A, 702B, 702C, or 702D.

With additional interpolated expected masses <NUM>, <NUM>, in accordance with various implementations of method <NUM>, for other predetermined fractions of the peak intensity, a lower <NUM> and/or upper <NUM> flank width of the bandpass characteristic <NUM> of the static field mass filter <NUM> may be determined.

For example, a second difference <NUM> between the first lower expected mass <NUM> and the third lower expected mass <NUM> may be determined. Based on the second difference <NUM>, a lower flank width <NUM> of the bandpass characteristic <NUM> of the static field mass filter <NUM> may be determined for the at least one ion species <NUM> of interest.

As another example, a third difference <NUM> between the first higher expected mass <NUM> and the third higher expected mass <NUM> may be determined. Based on the third difference <NUM>, an upper flank width <NUM> of the bandpass characteristic <NUM> of the static field mass filter <NUM> may be determined for the at least one ion species <NUM> of interest.

In some implementations described herein, method <NUM> may comprise displaying one or more of the interpolated lower <NUM>, <NUM> and higher <NUM>, <NUM> expected masses. Method <NUM> may also comprise displaying one or more of the determined lower <NUM>, <NUM> and higher <NUM>, <NUM> masses. Method <NUM> may, additionally or alternatively, comprise displaying the transmission windows of the interpolated lower and higher expected masses.

Referring to <FIG>, the mass spectrometer comprises a static field mass filter <NUM> which comprises a first Wien filter <NUM> and a second Wien filter <NUM>. A Wien filter combines magnetic and electric fields. While the electric field of the first Wien filter <NUM> deflects all ions irrespective of their mass <NUM>, the deflection caused by the magnetic field is higher for light ions 701C than for heavy ions 701A. Applying a magnetic field therefore leads to a mass dispersion. Since ions of all masses are deflected (only with a different deflection magnitude), no ion stays in the ion optical centre and therefore no ion is transmitted through the Wien filters <NUM>, <NUM>. However, by applying an electric field with the opposite direction in the second Wien filter <NUM> at the same time, the effect of the magnetic field can be cancelled out for one specific mass (e.g., <NUM>), while all other masses are deflected. Ions with a mass similar to the centre mass stay close to the centre axis of the ion optics and are transmitted well trough the Wien filters <NUM>, <NUM>. Ions with a significantly different masses have a lower transmission. And ions with even greater mass differences are not transmitted at all.

Referring to <FIG>, herein, the mass ranges may be referred to as:.

The entire transmission of the static field mass filter may be called bandpass (or bandpass characteristic <NUM> of the static field mass filter <NUM>).

In the example of <FIG>, the parameters to adjust the behaviour of the Wien filters <NUM>, <NUM> are the magnetic field (B-field) and the electric field (E-field). To predict the transmission of the double Wien filter <NUM> and <NUM>, a calibration is useful showing which masses have which transmission at given transmission window conditions (B-field 702A, 702B, 702C, 702D / E-field <NUM>, <NUM>, <NUM>, <NUM> combinations).

A solution for the problem is to adjust the pre-filter and measure the transmission efficiency of a row of elements that are distributed (preferably approximately evenly) across the mass range of interest, repeating the measurements for various pre-filter settings. For example, the pre-filter transmission curve is measured for five settings (centre masses <NUM>, <NUM>, <NUM>, <NUM>, <NUM>). The result may be a mass spectrum with analyte masses of equal or known intensity to characterize bandpass shape, before activation of the pre-filter. Analyte masses after sequentially tuning the pre-filter with centre masses to <NUM>, <NUM>, <NUM>, <NUM>, and <NUM> amu may then be obtained. This solution is very complex since it requires a sample solution that contains basically an isotope on every mass, because different combinations of window width and centre masses would cover more or less the whole periodic table, and is therefore very time consuming.

The approach described solves the problem by providing a simple way to measure the raw data for a calibration curve and how it can be converted into the calibration curves.

A benefit of the present approach is that it can extrapolate the transmission curve <NUM> for all pre-filter settings from a limited number of measurements. Besides a reduced number of measurements compared with the intuitive solution, the approach of the present disclosure has the benefit that no special sample solution is required. All necessary measurements can be done with a standard tuning solution (i.e., with fewer elements/ion species).

<FIG> shows an example of measured data according to the present approach. Columns D to G contain the E-field values for <NUM>% <NUM>, <NUM> and <NUM>% <NUM>, <NUM> transmission of the various centre mass/window width combinations.

A mass calibration curve may be determined as follows.

Referring again to <FIG>, column I contains the average E-fields. Column J shows the E-fields based on the ion optical theory of the double Wien filter: UWien = const · √(E/m) · d · B.

This equation is one option for the first predetermined relationship.

To account for the differences between the theoretical values (in column J) and the average measured values (in column I) a correction factor is introduced (in column K).

The correction factor may be obtained by fitting a polynomial function of the magnetic field B to account for the above-mentioned difference. The correction factor should remain the same for a given mass spectrometer and for a given value of the magnetic field B in various operational regimes.

After fitting the correction factor to the magnetic field, the mass calibration function is as follows: <MAT> where C<NUM>, C<NUM>, C<NUM> are the polynomial fit coefficients for the correction factor. This equation is another option for the first predetermined relationship.

With this equation the corresponding E-field for a given centre mass and B-field can be calculated. After rearranging, the equation gives the corresponding centre mass <NUM>, <NUM>, <NUM>, <NUM> for a given E-field <NUM>, <NUM>, <NUM>, <NUM>/B-field 702A, 702B, 702C, 702D combination.

The calculation of <NUM>% (first predetermined fraction) and <NUM>% (second predetermined fraction) transmission curves for the low <NUM> mass and high mass flank <NUM> for a given B-field strength <NUM> may be performed as follows.

Referring to <FIG>, after calculating the mass calibration the collected E-field values <NUM>, <NUM>, <NUM>, <NUM> (in columns D to G) can be translated into masses <NUM>, <NUM>, <NUM>, <NUM> (in columns J to K).

As an example, <FIG> indicates, with crosses on the curves, the measurement positions (<NUM>, <NUM>, <NUM>, <NUM>, 238amu of B35 to B38 in <FIG>) relative to the centre mass of the entire bandpass curve of the pre-filter for <NUM>% high mass side <NUM>, <NUM>% high mass side <NUM>, <NUM>% low mass side <NUM> and <NUM>% low mass side <NUM> (J35 to J38, K35 to K38, L35 to K38, M35 to M38) with <NUM>% B-field 702A.

For example, cell K25 in <FIG> means that the Nd signal is reduced to <NUM>% of its maximum intensity when the prefilter is set to a centre mass of <NUM>. 7amu at <NUM>% magnetic field strength. In comparison, a centre mass of <NUM>. 7amu is needed to reduce the Nd Signal to <NUM>% of its maximum intensity if a magnetic field strength of <NUM>% is set (see cell K18).

For example, interpolating between the masses with <NUM>% transmission on the low mass side <NUM> (plotted on the Y-Axis) for the corresponding centre masses (plotted on the X-Axis), e.g., <NUM>, leads to curve <NUM> in <FIG>, which shows the curves with <NUM>% transmission low mass side <NUM> (curve <NUM>), <NUM>% transmission low mass side <NUM> (curve <NUM>), <NUM>% transmission high mass side <NUM> (curve <NUM>) and <NUM>% transmission high mass side <NUM> (curve <NUM>).

The equation for the curves <NUM>, <NUM>, <NUM>, <NUM> has the form: <MAT> With C<NUM>, C<NUM>, C<NUM> being individual coefficients for a curve <NUM>, <NUM>, <NUM>, or <NUM> and X denoting the transmission fraction (e.g., <NUM>%).

From these curves the visualization of the transmission window can directly be derived:.

With these calculated values the transmission window can be visualized as shown in <FIG>, which shows a visualization of the pre-filter mass transmission <NUM> with 200amu centre mass <NUM> and <NUM>% B-field 702A.

An interpolation to all B-field values may be performed. The workflow described above explains how to calculate the transmission curve for a given B-field strength <NUM>. To be able to visualize the window also for all other magnetic field strengths, the curves showed in <FIG> (<NUM>% magnetic field strength 702A) are also obtained for <NUM>%, <NUM>% and <NUM>% magnetic field strength 702D, 702C, 702B. <FIG> shows how the width <NUM> of the centre changes for different B-fields <NUM>. Each curve is a polynomial fit function of the measurements showed in <FIG> with the coefficients C<NUM>, C<NUM>, C<NUM>. By fitting each of the coefficients to the B-field, the coefficients become a function of the B-field: <MAT>.

An advantage of this workflow is that tuning is not trial and error and the trade-off between a narrow <NUM> transmission window <NUM> with steep flanks <NUM>, <NUM> and a wide <NUM> window <NUM> with flat flanks <NUM>, <NUM> can be visualised.

Various operations may be described as multiple discrete actions or operations in turn, in a manner that is most helpful in understanding the subject matter disclosed herein. However, the order of description should not be construed as to imply that these operations are necessarily order dependent. In particular, these operations may not be performed in the order of presentation. Operations described may be performed in a different order from the described implementation. Various additional operations may be performed, and/or described operations may be omitted in additional implementations. Operations are illustrated once each and in a particular order in <FIG>, <FIG>, <FIG>, and <FIG>, but the operations may be reordered and/or repeated as desired and appropriate (e.g., different operations performed may be performed in parallel, as suitable).

The approaches described herein may be embodied on a computer-readable medium, which may be a non-transitory computer-readable medium. The computer-readable medium carries computer-readable instructions arranged for execution upon a processor so as to make the processor carry out any or all of the methods described herein.

The term "computer-readable medium" as used herein refers to any medium that stores data and/or instructions for causing a processor to operate in a specific manner. Such storage medium may comprise non-volatile media and/or volatile media. Non-volatile media may include, for example, optical or magnetic disks. Volatile media may include dynamic memory. Exemplary forms of storage medium include, a floppy disk, a flexible disk, a hard disk, a solid state drive, a magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with one or more patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EPROM, NVRAM, and any other memory chip or cartridge. The computer-readable medium may be transitory (e.g., a wire or a wireless propagation medium in which a signal is being transmitted) or non-transitory.

For the purposes of the present disclosure, the phrases "A and/or B" and "A or B" mean (A), (B), or (A and B). For the purposes of the present disclosure, the phrases "A, B, and/or C" and "A, B, or C" mean (A), (B), (C), (A and B), (A and C), (B and C), or (A, B, and C). Although some elements may be referred to in the singular (e.g., "a processing device"), any appropriate elements may be represented by multiple instances of that element, and vice versa. For example, a set of operations described as performed by a processing device may be implemented with different ones of the operations performed by different processing devices.

The description uses the phrases "an implementation", "various implementations", and "some implementations", each of which may refer to one or more of the same or different implementations. Furthermore, the terms "comprising", "including", "having", and the like, as used with respect to embodiments of the present disclosure, are synonymous. When used to describe a range of dimensions, the phrase "between X and Y" represents a range that includes X and Y. As used herein, an "apparatus" may refer to any individual device, collection of devices, part of a device, or collections of parts of devices.

Claim 1:
A method (<NUM>) of determining, for a target ion mass, a bandpass range of an ion filter for a mass spectrometer, the method comprising:
introducing (<NUM>) at least two ion species into the ion filter;
characterized in that the method further comprises:
scanning (<NUM>) a mass range of each ion species by changing at least one filter parameter;
determining (667a) an ion intensity associated with each scanned mass using the mass spectrometer;
registering (667b) the ion intensities and the associated filter parameters of each scanned mass range;
for each scanned mass range, determining (<NUM>) a maximum ion intensity and the associated centre mass;
for each scanned mass range, determining (<NUM>, <NUM>):
a first mass associated with a first ion intensity corresponding with a first fraction of the respective maximum intensity, and
a second mass associated with a second ion intensity corresponding with a second fraction of the respective maximum intensity,
wherein the first mass is lower than the centre mass of the ion species and the second mass is higher than the centre mass of the ion species; and
for the target ion mass having a known centre mass, deriving (<NUM>) the masses having the associated first ion intensity and second ion intensity from the determined first ion intensities and second ion intensities of the scanned masses, wherein the deriving comprises:
interpolating, from the first mass for each of the at least two ion species, a first expected mass having the associated first ion intensity using a first interpolant function; and
interpolating, from the second mass for each of the at least two ion species, a second expected mass having the associated second ion intensity using a second interpolant function.