Comprehensive two-dimensional chromatography employs a serially coupled two-column arrangement where effluent from the first column is collected or sampled and then introduced to the second column according to a chosen modulation period. This is effected by use of a modulator at or near the column junction. One of the considerations in applying the technique is the period of the modulator, which determines the sampling duration of the first column effluent. Here, we propose that the sampling rate can be most effectively described by a new term, called the modulation ratio (MR). This is defined as the ratio of 4 times the first column peak standard deviation (4sigma) divided by the modulation period (PM) or 1.6985 times the half-height width of the peak (wh): MR = 4sigma/PM = wb/PM = (wh x 1.6985)/PM. The 4sigma value is more commonly recognized as the peak base width (wb). The use of 4sigma as the numerator is preferred to simply sigma because when the PM value used for an experiment is equal to sigma, then the MR value is calculated to be 4, implying that the primary peak will be modulated approximately 4 times as is normally recommended for a comprehensive multidimensional separation. The less well-defined term of modulation number (NM) has been previously used and proposed as the number of modulations per peak and, therefore, is intended to convey the manner in which the primary column peak is sampled; this is a subjective and not well-characterized value. The use of MR should provide users with a meaningful and strictly defined value when reporting experimental conditions. The utility of MR is demonstrated through a mathematical model of the modulation process for both Gaussian and tailing peaks, supported by an experimental study of the modulation ratio. It is shown that for the analysis of trace compounds where precise quantitative measurements are being made, the experiment should be conducted with an MR of at least 3. Conversely, for semiquantitative methods or the analysis of major components, an MR of approximately 1.5 should suffice.

A method to predict the crystal structure of equiatomic ternary compositions based only on the constituent elements was developed using cluster resolution feature selection (CR-FS) and support vector machine (SVM) classification. The supervised machine-learning model was first trained with 1037 individual compounds that adopt the most populated ternary 1:1:1 structure types (TiNiSi-, ZrNiAl-, PbFCl-, LiGaGe-, YPtAs-, UGeTe-, and LaPtSi-type) and then validated using an additional 519 compounds. The CR-FS algorithm improves class discrimination and indicates that 113 variables including size, electronegativity, number of valence electrons, and position on the periodic table (group number) influence the structure preference. The final model prediction sensitivity, specificity, and accuracy were 97.3%, 93.9%, and 96.9%, respectively, establishing that this method is capable of reliably predicting the crystal structure given only its composition. The power of CR-FS and SVM classification is further demonstrated by segregating the crystal structure of polymorphs, specifically to examine polymorphism in TiNiSi- and ZrNiAl-type structures. Analyzing 19 compositions that are experimentally reported in both structure types, this machine-learning model correctly identifies, with high confidence (>0.7), the low-temperature polymorph from its high-temperature form. Interestingly, machine learning also reveals that certain compositions cannot be clearly differentiated and lie in a "confused" region (0.3-0.7 confidence), suggesting that both polymorphs may be observed in a single sample at certain experimental conditions. The ensuing synthesis and characterization of TiFeP adopting both TiNiSi- and ZrNiAl-type structures in a single sample, even after long annealing times (3 months), validate the occurrence of the region of structural uncertainty predicted by machine learning.

For a technology little over a decade old, comprehensive two-dimensional gas chromatography (GC x GC) has quickly reached the status of one of the most powerful analytical tools for volatile organic compounds. At the heart of any GC x GC system is an interface, which physically connects the primary and the secondary columns and acts to preserve the separation obtained in the first dimension (first column) while allowing additional separation in the second dimension. The paper presents a review of the technology, including fundamental principles of the technique, data processing and interpretation and a timeline of inventive contributions to interface design. In addition, applications of the technique are presented, with a more detailed discussion of selected examples.

Partial least-squares discriminant analysis (PLS-DA) and support vector machine (SVM) techniques were applied to develop a crystal structure predictor for binary AB compounds. Models were trained and validated on the basis of the classification of 706 AB compounds adopting the seven most common structure types (CsCl, NaCl, ZnS, CuAu, TlI, β-FeB, and NiAs), through data extracted from Pearson’s Crystal Data and ASM Alloy Phase Diagram Database. Out of 56 initial variables (descriptors based on elemental properties only), 31 were selected in as unbiased manner as possible through a procedure of forward selection and backward elimination, with the quality of the model evaluated by measuring the cluster resolution at each step. PLS-DA gave sensitivity of 96.5%, specificity of 66.0%, and accuracy of 77.1% for the validation set data, whereas SVM gave sensitivity of 94.2%, specificity of 92.7%, and accuracy of 93.2%, a significant improvement. Radii, electronegativity, and valence electrons, previously chosen intuitively in structure maps, were confirmed as important variables. PLS-DA and SVM could also make quantitative predictions of hypothetical compounds, unlike semiclassical approaches. The new compound RhCd was predicted to have the CsCl-type structure by PLS-DA (0.669 probability) and, at an even stronger confidence level, by SVM (0.918 probability). RhCd was synthesized by reaction of the elements at 800 °C and confirmed by X-ray diffraction to adopt the CsCl-type structure. SVM is thus a superior classification method in crystallography that is fast and makes correct, quantitative predictions; it may be more broadly applicable to help identify the structure of unknown compounds with any arbitrary composition.

Indole-3-acetic acid (IAA) is an auxin produced by terrestrial plants which influences development through a variety of cellular mechanisms, such as altering cell orientation, organ development, fertility, and cell elongation. IAA is also produced by bacterial pathogens and symbionts of plants and algae, allowing them to manipulate growth and development of their host. They do so by either producing excess exogenous IAA or hijacking the IAA biosynthesis pathway of their host. The endogenous production of IAA by algae remains contentious. Using Emiliania huxleyi, a globally abundant marine haptophyte, we investigated the presence and potential role of IAA in algae. Homologs of genes involved in several tryptophan-dependent IAA biosynthesis pathways were identified in E. huxleyi. This suggests that this haptophyte can synthesize IAA using various precursors derived from tryptophan. Addition of L-tryptophan to E. huxleyi stimulated IAA production, which could be detected using Salkowski's reagent and GC × GC-TOFMS in the C cell type (coccolith bearing), but not in the N cell type (bald). Various concentrations of IAA were exogenously added to these two cell types to identify a physiological response in E. huxleyi. The N cell type, which did not produce IAA, was more sensitive to it, showing an increased variation in cell size, membrane permeability, and a corresponding increase in the photosynthetic potential quantum yield of Photosystem II (PSII). A roseobacter (bacteria commonly associated with E. huxleyi) Ruegeria sp. R11, previously shown to produce IAA, was co-cultured with E. huxleyi C and N cells. IAA could not be detected from these co-cultures, and even when stimulated by addition of L-tryptophan, they produced less IAA than axenic C type culture similarly induced. This suggests that IAA plays a novel role signaling between different E. huxleyi cell types, rather than between a bacteria and its algal host.