ABSTRACT
The Generative Pre-Trained Transformer known as ChatGPT-4 has undergone extensive pretraining on a diverse data set, enabling it to generate coherent and contextually relevant text based on the input it receives. This capability allows it to perform tasks from answering questions and has attracted significant interest in material sciences, synthetic chemistry, and drug discovery. In this work, we posed four advanced task-specific problems to ChatGPT, which were recently published in leading journals for topics in analytical chemistry, spectroscopy, bioimage super-resolution, and electrochemistry. ChatGPT-4 successfully implemented the four ideas after assigning the "persona" to the AI and posing targeted questions. We show two cases where "unguided" ChatGPT could complete the assignments with minimal human direction. The construction of a microwave spectrum from a free induction curve and super-resolution of bioimages was accomplished using this approach. Two other specific tasks, correcting a complex baseline with morphological operations of set theory and estimating the diffusion current, required additional input, e.g., equations and specific directions from the user. In each case, the MATLAB code was eventually generated by ChatGPT-4 even when the original authors did not provide any code themselves. We show that a validation test must be implemented to detect and correct mistakes made by ChatGPT-4, followed by feedback correction. These approaches will pave the way for open and transparent science and eliminate the black boxes in measurement science when it comes to advanced data processing.
ABSTRACT
With the introduction of ultrahigh efficiency columns and fast separations, the need to eliminate peak deformation contributed by the instrument must be effectively solved. Herein, we develop a robust framework to automate deconvolution and minimize its artifacts, such as negative dips, wild noise oscillations, and ringing, by combining regularized deconvolution and Perona-Malik (PM) anisotropic diffusion methods. A asymmetric generalized normal (AGN) function is proposed to model the instrumental response for the first time. With no-column data at various flow rates, the interior point optimization algorithm extracts the parameters describing instrumental distortion. The column-only chromatogram was reconstructed using the Tikhonov regularization technique with minimal instrumental distortion. For illustration, four different chromatography systems are used in fast chiral and achiral separations with 2.1 and 4.6 mm i.d. columns. Ordinary HPLC data can approach highly optimized UHPLC data. Similarly, in fast HPLC-circular dichroism (CD) detection, 8000 plates were gained for a fast chiral separation. Moment analysis of deconvolved peaks confirms correction of the center of mass, variance, skew, and kurtosis. This approach can be easily integrated and used with virtually any separation and detection system to provide enhanced analytical data.
ABSTRACT
A majority of enantiomeric separations show some degree of peak asymmetry, which is detrimental to quantitative and semiquantitative chiral analysis. This paper presents a simple and rapid peak symmetrization algorithm for the correction or reduction of peak tailing or fronting in exponentially modified Gaussians. Raw chromatographic data can be symmetrized by adding a correct fraction of the first derivative to the chromatogram. The area remains invariant since the area under the first derivative is zero for a pure Gaussian and numerically close to zero for asymmetric peaks. A method of easily extracting the distortion parameter is provided, as well as insight into how pre-smoothing the data with the "perfect smoother" algorithm can suppress high frequencies effectively. The central difference method is also used to compute the first derivative, reducing root-mean-square noise by up to 28% compared to the standard forward difference method. A survey of 40 chiral separations is presented, demonstrating the range of asymmetry observed in chiral separations. Examples of symmetrization of the peaks from enantiomers in comparable and disproportionate concentrations are also provided. Artifacts of deconvolution are discussed, along with methods to mitigate such artifacts.
Subject(s)
Algorithms , Chromatography , Chromatography/methods , Stereoisomerism , ArtifactsABSTRACT
Sub/supercritical fluid chromatography (SFC) is a green separation technique that has been used to separate a wide variety of compounds and is proven to be immensely useful for chiral separations. However, SFC is currently not thought to be applicable for ionic compounds due to their low solubility in CO2, even with additives and organic modifiers. Recently, a large amount of research has been centered on octahedral complexes of Ru(II) and Os(II) with bidentate polypyridyl ligands due to their ability to serve in cancer treatment and other biological activities. These compounds exist as the delta (Δ) and lambda (Λ) enantiomers. Previously, similar compounds have been enantiomerically separated using HPLC and capillary electrophoresis, but never with SFC. Cyclofructan-6 (CF6) derivatized with (R)-naphthyl ethyl (RN) groups has been proven to be an effective chiral stationary phase for these separations in HPLC. This column chemistry was expanded to SFC to provide the first chiral separation of a wide variety (23 complexes in total) of ionic octahedral polypyridyl complexes. Unexpected behavior for mixing methanol and acetonitrile as the organic modifier will be discussed, along with the effects of additives. Enantioselectivity on CF6-RN chemistry is shown to be dependent on the conjugation level and rigidity of the metal complexes. Mass transfer kinetic behavior is also shown, and high-efficiency baseline resolved rapid separations are shown for fast screening or quantitation of representative coordination complexes.