| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Studies analytical methods that rely on scientific instruments for detection, identification, and quantification of chemical species; excludes purely classical wet-chemistry tests or non-instrumented qualitative analysis. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates from atomic and molecular scales (ionization, excitation, absorption, emission) to macroscopic instrument platforms (chromatographs, spectrometers, electrochemical analyzers). |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | Analytes, photons, ions, electrons, detectors, transducers, instrumental components, noise sources, calibration standards, signals, baselines, matrix components. |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Absorbance, emission intensity, mass/charge ratio, retention time, signal intensity, resolution, noise level, selectivity, response factor, sensitivity, dynamic range, drift characteristics. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Spectroscopic (IR, UV–Vis, fluorescence), mass spectrometric, chromatographic, electroanalytical, thermal analytical, atomic spectrometric, hyphenated techniques (GC–MS, LC–MS). |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | Wavelength, frequency, m/z, voltage, current, temperature, pressure, flow rate, signal intensity, baseline level, integration time, detector gain, analyte concentration, instrument mode. |
| | Parameterization | How variables encode and represent the system’s state. | States encoded via calibration curves, response functions, instrument-transfer functions, resolution metrics, detector sensitivity curves, noise models, signal-processing parameters. |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Ideal detector linearity, zero baseline drift, negligible noise, perfect resolution, uniform ionization/excitation efficiency, stable temperature and flow, interference-free matrix behavior. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | Valid for well-calibrated, stable instruments; break down when drift, matrix suppression, detector saturation, nonlinear response, temperature/pressure fluctuations, or interfering species dominate. |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | Instrument response is predictable; noise can be statistically characterized; calibration captures analyte–signal relationships; underlying physical laws (Beer–Lambert, ion optics, etc.) govern behavior. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes reproducible instrument performance, meaningful signal-to-noise ratios, stable standards, proper maintenance, and appropriate statistical models for uncertainty and noise. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Requires coherence between instrument physics, calibration models, detector behavior, signal processing, and sample properties without contradictions. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Demands alignment of chemical behavior, instrumental physics, detector characteristics, and computational processing within one unified analytical workflow. |
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | Absorbance/emission peaks, m/z ion signals, chromatographic peaks, voltammograms, current/potential curves, thermal transitions, resonance frequencies, scattering signals, detector counts, baseline drift. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Limited by detector sensitivity, signal-to-noise ratio, ionization efficiency, matrix suppression, optical scattering, thermal noise, resolution limits, dynamic range, and interference from co-eluting or overlapping species. |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | Absorbance (a.u.), wavelength (nm), frequency (Hz or cm⁻¹), m/z, retention time (min), potential (V), current (A), temperature (°C/K), signal intensity (counts), mass/volume units, time (s). |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | UV–Vis, IR, Raman, NMR, MS, GC/LC-MS, ICP-MS/OES, fluorescence spectrometers, electrochemical analyzers, thermal analyzers (DSC/TGA), XRD, XPS, ESR/EPR, TOF detectors, CCD sensors, interferometers. |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Absorbance defined by Beer–Lambert relations; retention time by chromatographic elution; m/z by MS detector calibration; electrochemical signals by current–potential response; baseline by detector output absent analyte. |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | Wavelength/mass scans, gradient programs, ionization sequences, pulse settings, applied voltage ramps, NMR acquisition sequences, thermal ramp methods, calibration runs, blank corrections, standard injections. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Multi-scan spectral acquisition, chromatographic runs, MS fragmentation scans, electrochemical sweeps, time-series sampling, multi-frequency NMR experiments, scanning/waveform averaging, signal integration routines. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Replicate injections, multi-region sampling, split-sample replicates, multiple detector modes, different ionization energies, multiple wavelengths, dynamic scanning, temporal sampling for kinetic measurements. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | Spectra, chromatograms, electropherograms, mass spectra, thermal curves, electrochemical voltammograms, scattering/absorption maps, NMR FID signals, time-series measurements, instrument logs. |
| | Resolution | The granularity or precision with which data is captured. | Determined by detector bandwidth, sampling rate, optical/magnetic/electric field stability, mass analyzer resolution (FWHM), chromatographic efficiency, temperature control precision, signal discretization, and noise floors. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Wavelength calibration, mass-axis calibration, RF/pulse calibration (NMR), detector gain calibration, baseline correction, reference-standard injections, flow/pressure/temperature verification, instrument validation checks. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Identifying noise sources (shot noise, flicker noise, drift), matrix effects, detector saturation, baseline instability, ion suppression, optical scattering, misalignment, signal clipping, integration errors, and instrument aging. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Beer–Lambert law, mass spectrometric ion-abundance relationships, chromatographic retention laws, Nernst electrochemical relations, resonance conditions (NMR/EPR), instrument response functions, noise laws (Poisson/Gaussian). |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Stable calibration slopes under fixed conditions, invariant mass/charge ratios, consistent spectral fingerprints, reproducible retention times under identical conditions, conserved physical constants in detector response. |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | Absorption/emission, ionization, fragmentation, electron/ion detection, redox reactions at electrodes, chromatographic partitioning, magnetic resonance excitation, thermal decomposition/differentiation. |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Signal-generation pathways (ionization → separation → detection), excitation–relaxation sequences, chromatographic elution pathways, fragmentation trees, electrochemical redox cycles, thermal decomposition sequences. |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Sensitivity, selectivity, resolution, dynamic range, signal-to-noise ratio, baseline drift, response factor, limit of detection (LOD), limit of quantification (LOQ), fragmentation pattern, retention factor, relaxation times (T₁/T₂). |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Spectroscopic methods, chromatographic methods, mass spectrometric methods, electroanalytical methods, thermal analysis, atomic spectrometry, hyphenated techniques, detector classes (optical, electrochemical, MS, thermal). |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | Beer–Lambert equation, Nernst equation, NMR resonance equations, MS ion kinetic equations, chromatographic retention/plate equations, electrochemical peak equations, noise/uncertainty models, calibration regression equations. |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Response-function models, noise models (white, pink, shot), chromatographic plate and rate theory, MS fragmentation models, detector-efficiency models, resonance models, thermal decomposition models. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Perfectly linear response, zero drift, infinite resolution, uniform detector sensitivity, ideal Gaussian peaks, fragmentation without secondary reactions, no matrix effects, constant temperature and flow. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Break down with instrument drift, detector saturation, non-linear response, co-eluting peaks, matrix suppression, unstable baselines, field inhomogeneity, temperature gradients, or poor ionization efficiency. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Integration of spectroscopy, chromatography, mass spectrometry, electrochemistry, and signal processing into a unified framework connecting physical principles, detector physics, and analytical performance. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Connects to physics (optics, magnetism, electronics), engineering (instrument design, control systems), statistics (signal processing, regression), materials science (detectors), and computer science (data analysis, algorithms). |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Controlling wavelength, current/voltage, flow rate, temperature, ionization source parameters, detector gain, scan speed, sample preparation, injection volume, and instrument calibration to interrogate analyte–signal relationships. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Monitoring natural baseline drift, detector warm-up behavior, solvent-front shifts, aging of lamps/detectors, passive noise changes, contamination accumulation, and matrix-driven suppression/enhancement without intentional manipulation. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Comparing predicted spectra, chromatograms, mass distributions, voltammograms, resonance frequencies, and thermal transitions to measured data; validating instrument response models and calibration curves. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Performing replicate injections, repeated scans, multi-day drift checks, calibration verification runs, retention-time consistency checks, reproducibility measurements across operators/instruments/labs. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Applying regression to calibration curves, calculating confidence intervals, detecting outliers, quantifying noise distributions, determining LOD/LOQ, correcting drift, and estimating uncertainty of measured signals. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Evaluating linear vs nonlinear response models, comparing ionization models, signal-processing algorithms, chromatographic peak models, thermal decomposition models, and instrumental transfer-function predictions. |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Identifying and quantifying noise sources, baseline instability, detector saturation, mass-bias effects, optical scattering, flow-rate errors, ion suppression, temperature drift, misalignment, and integration errors. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Randomizing run order, applying blanks and controls, using internal standards, shielding instruments from environmental fluctuations, standardizing sample prep, stabilizing temperature/pressure, blinding spectral interpretation. |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Independent examination of raw spectra, chromatograms, mass spectra, calibration curves, instrument logs, signal-processing code, uncertainty models, and claimed detection/quantification capabilities. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Updating calibration models, modifying instrumental settings, adjusting signal-processing algorithms, revising physical assumptions behind ionization/detection, redefining resolution or sensitivity metrics based on new evidence. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Full disclosure of calibration data, raw instrument outputs, processing pipelines, instrument configuration, environmental conditions, uncertainty budgets, and assumptions behind signal interpretation. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Honest reporting of drifts, failures, noise issues, detection limits, instrument malfunctions, sample contamination, matrix interferences, and complete traceability of analytical results. |