Atom Counting Research Articles

Nanoplastics: Immune Impact, Detection, and Internalization after Human Blood Exposure by Single-Cell Mass Cytometry.

The increasing exposure to nanoplastics (NPs) raises significant concerns for human health, primarily due to their potential bioaccumulative properties. While NPs have recently been detected in human blood, their interactions with specific immune cell subtypes and their impact on immune regulation remain unclear. In this proof-of-concept study, model palladium-doped polystyrene NPs (PS-Pd NPs) are utilized to enable single-cell mass cytometry (CyTOF) detection. The size-dependent impact of carboxylate polystyrene NPs (50-200 nm) is investigated across 15 primary immune cell subpopulations using CyTOF. By taking advantage of Pd-doping for detecting PS-Pd NPs, this work evaluates their impact on human immune-cells at the single-cell level following blood exposure. This work traces PS-Pd NPs in 37 primary immune-cell subpopulations from human blood, quantifying the palladium atom count per cell by CyTOF while simultaneously assessing the impact of PS-Pd NPs on cell viability, functionality, and uptake. These results demonstrate that NPs can interact with, interfere with, and translocate into several immune cell subpopulations after exposure. In vivo distribution experiments in mice further confirmed their accumulation in immune cells within the liver, blood, and spleen, particularly in monocytes, macrophages, and dendritic cells. These findings provide valuable insights into the impact of NPs on human health.

Surgical dissection of ignition behavior of aluminum nanoparticles using molecular dynamics

Aluminum nanoparticles (ANPs) show great promise in energy technology, yet their atomic behavior during ignition remains a puzzle. This study provides insights into atomic migration processes within ANPs of different chemical layers during ignition and combustion, inspired by surgical peeling techniques. The research reveals that cavity formation in ANPs is inevitable due to disparities in atomic migration rates between core and surface areas, with core aluminum atoms experiencing sudden and significant stress alterations crucial for cavity formation. A linear trend in atom counts across radial layers correlates with particle geometry, influencing reactivity and morphological changes. The proposed Bond Environment Change (BEC) analysis reveals that oxygen atoms in the ANP shell undergo a three-phase evolution: intensive bonding, gradual relaxation, and stable configuration, reflecting the dynamic transformation of the oxide layer during the reaction process. While this research provides a deeper understanding of ANP ignition and combustion mechanisms, it may serve as a reference for refining ANP synthesis and guiding further inquiries in the field of nanoparticle-based energy technologies.

Obtaining 3D Atomic Reconstructions from Electron Microscopy Images Using a Bayesian Genetic Algorithm: Possibilities, Insights, and Limitations.

The Bayesian genetic algorithm (BGA) is a powerful tool to reconstruct the 3D structure of mono-atomic single-crystalline metallic nanoparticles imaged using annular dark field scanning transmission electron microscopy. The number of atoms in a projected atomic column in the image is used as input to obtain an accurate and atomically precise reconstruction of the nanoparticle, taking prior knowledge and the finite precision of atom counting into account. However, as the number of parameters required to describe a nanoparticle with atomic detail rises quickly with the size of the studied particle, the computational costs of the BGA rise to prohibitively expensive levels. In this study, we investigate these computational costs and propose methods and control parameters for efficient application of the algorithm to nanoparticles of at least up to 10 nm in size.

Towards atom counting from first moment STEM images: methodology and possibilities

Through a simulation-based study we develop a statistical model-based quantification method for atomic resolution first moment scanning transmission electron microscopy (STEM) images. This method uses the uniformly weighted least squares estimator to determine the unknown structure parameters of the images and to isolate contributions from individual atomic columns. In this way, a quantification of the projected potential per atomic column is achieved. Since the integrated projected potential of an atomic column scales linearly with the number of atoms it contains, it can serve as a basis for atom counting. The performance of atom counting from first moment STEM imaging is compared to that from traditional HAADF STEM in the presence of noise. Through this comparison, we demonstrate the advantage of first moment STEM images to attain more precise atom counts. Finally, we compare the integrated potential extracted from first-moment images of a wedge-shaped sample to those values from the bulk crystal. The excellent agreement found between these values proves the robustness of using bulk crystal simulations as a reference library. This enables atom counting for samples with different shapes by comparison with these library values.

Nitrogen-rich sulfur-containing heterocyclic adsorbents for effective selective adsorption of Au(III)/Pd(II)/Pt(IV)

Recovering precious metals from secondary resources has elicited considerable attention, and exploring an efficient adsorbent is a challenge. In this work, we have specifically tailored three highly effective adsorbents (THDA@PEI, DCTA@PEI and DCTD@PEI) by crosslinking PEI with sulfur-containing heterocyclic cross-linking agents (2,5-thiophenedicarboxaldehyde, 2,4-dichlorothiazole and 3,4-dichloro-1,2,5-thiadiazole) for recovery of Au(III), Pd(II), and Pt(IV). These agents are distinguished by a range of nitrogen atom counts that extend from zero to two, offering a foundation for exploring the correlation between their molecular structure and adsorption performance. THDA@PEI, DCTA@PEI, and DCTD@PEI exhibit remarkable adsorption capacities for Au(III), Pd(II), and Pt(IV), with values reaching up to 2169, 2240, and 2536 mg/g for Au(III), 771, 886, and 846 mg/g for Pd(II), and 832, 1021, and 887 mg/g for Pt(IV), respectively. Among the three materials, DCTD@PEI stands out with a broader pH applicability range for both gold (pH 2 to 9) and platinum (pH 1 to 11) adsorption and exhibits a more rapid adsorption rate for Au and Pd. The adsorption processes of the three adsorbents on precious metals are spontaneous and entropy-increasing processes. Increasing the adsorption temperature could improve the adsorption performance of the three adsorbents, except for Pd adsorption by DCTD@PEI. All of the three adsorbents exhibit good selectivity and reusability for Au, Pd, and Pt, with DCTA@PEI being the most outstanding. It is observed that an increase in nitrogen content in the adsorbents correlates positively with the improvement of key performance indicators, including a wider pH range, higher adsorption capacity, faster kinetics, better selectivity, and enhanced reusability, while the substituent positions of crosslinking agents also have a strong impact on the adsorption performance, especially for Pd and Pt.

Viscosity of Asphalt Binder through Equilibrium and Non-Equilibrium Molecular Dynamics Simulations

Viscosity is a curial indicator for evaluating asphalt performance, representing its ability to resist deformation under external forces. The Green–Kubo integral in equilibrium molecular dynamics simulations and the Muller-Plathe algorithm in reverse non-equilibrium molecular dynamics simulations were used to calculate the asphalt viscosity. Meanwhile, the key parameters of both methods were rationalized. The results show that in equilibrium calculations, using a 1/t weighting for the viscosity integral curve results in a well-fitted curve that closely matches the original data. The isotropy of the asphalt model improves for atomic counts exceeding 260,000, rendering viscosity calculations more reasonable. When the viscosity did not converge, it increased linearly with the number of atoms. In non-equilibrium calculations, the number of region divisions had almost no effect on the viscosity value. A momentum exchange period of 20 timesteps exhibits a favorable linear trend in velocity gradients, and an ideal momentum exchange period was found to be between 10 and 20 timesteps. As the model size increased, the linear relationship with the shear rate became more pronounced, and the isotropy of the asphalt system improved. Using an orthogonal simulation box with a side length of 75 Å effectively meets the computational requirements.

Performance of catalytic wet oxidation on thermochemical aqueous effluents assessed by FT-ICR MS

Aqueous phase (AP) is frequently observed in bio-oil from biomass thermochemical conversion or as an upgrading coproduct. However, AP contains high organic content that can be toxic and that must be reduced or removed to be then treated in existing wastewater plants. A promising method to upgrade these APs is the catalytic wet peroxide oxidation (CWPO) that was applied here on APs of both hydrothermal liquefaction and pyrolysis bio-oils from softwood and hardwood. Efficiency of the CWPO was assessed by investigating the molecular composition of raw and treated APs by ultra-high-resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) with electrospray ionization (ESI) in negative- and positive-ion modes. Combination of FT-ICR MS and multivariate statistical analyses evidenced efficiency of the process with new compounds resulting from the oxidative depolymerization, characterized by lower unsaturation degree and molecular weight, and higher oxygen atom count. Refractory compounds were also observed, which are notably characterized by high unsaturation degrees. Interestingly, both detection modes were shown to be complementary for the understanding of the CWPO process. ESI (-) allowed to assess the completion of the oxidation process in detecting carboxylic acid conversion products and to specifically evidence refractory products. ESI (+) was informative on the CWPO process, especially on the conversion of the basic compounds, with the decrease of the detected species. Eventually, softwood AP was shown to be more reluctant to CWPO than the hardwood one.

Reaction rebalancing: a novel approach to curating reaction databases

PurposeReaction databases are a key resource for a wide variety of applications in computational chemistry and biochemistry, including Computer-aided Synthesis Planning (CASP) and the large-scale analysis of metabolic networks. The full potential of these resources can only be realized if datasets are accurate and complete. Missing co-reactants and co-products, i.e., unbalanced reactions, however, are the rule rather than the exception. The curation and correction of such incomplete entries is thus an urgent need.MethodsThe SynRBL framework addresses this issue with a dual-strategy: a rule-based method for non-carbon compounds, using atomic symbols and counts for prediction, alongside a Maximum Common Subgraph (MCS)-based technique for carbon compounds, aimed at aligning reactants and products to infer missing entities.ResultsThe rule-based method exceeded 99% accuracy, while MCS-based accuracy varied from 81.19 to 99.33%, depending on reaction properties. Furthermore, an applicability domain and a machine learning scoring function were devised to quantify prediction confidence. The overall efficacy of this framework was delineated through its success rate and accuracy metrics, which spanned from 89.83 to 99.75% and 90.85 to 99.05%, respectively.ConclusionThe SynRBL framework offers a novel solution for recalibrating chemical reactions, significantly enhancing reaction completeness. With rigorous validation, it achieved groundbreaking accuracy in reaction rebalancing. This sets the stage for future improvement in particular of atom-atom mapping techniques as well as of downstream tasks such as automated synthesis planning.Scientific ContributionSynRBL features a novel computational approach to correcting unbalanced entries in chemical reaction databases. By combining heuristic rules for inferring non-carbon compounds and common subgraph searches to address carbon unbalance, SynRBL successfully addresses most instances of this problem, which affects the majority of data in most large-scale resources. Compared to alternative solutions, SynRBL achieves a dramatic increase in both success rate and accurary, and provides the first freely available open source solution for this problem.

Spatial heterogeneity, sediment-water exchange, and diffusion mechanisms of organophosphate esters from river to coastal aquatic system in a typical economic circle of northern China

Organophosphate esters (OPEs) have proven to be pervasive in aquatic environments globally. However, understanding their partitioning behavior and mechanisms at the sediment-water interface remains limited. This study elucidated the spatial heterogeneity, interfacial exchange, and diffusion mechanisms of 14 OPEs (∑14OPEs) from river to coastal aquatic system. The transport tendencies of OPEs at the sediment-water interface were quantitatively assessed using fugacity methods. The total ∑14OPEs concentrations in water and sediments ranged from 154 ng/L to 528 ng/L and 2.41 ng/g dry weight (dw) to 230 ng/g dw, respectively. This result indicated a descending spatial tendency with moderate variability. OPE distribution was primarily influenced by temperature, pH, and dissolved oxygen levels. As the carbon atom number increased, alkyl and chlorinated OPEs transitioned from diffusion towards the aqueous phase to equilibrium. In contrast, aryl OPEs and triphenylphosphine oxide, which had equivalent carbon atom counts, maintained equilibrium throughout. Diffusion trends of individual OPE congener at the sediment-water interface varied at the same total organic carbon contents (foc). As the foc increased, the fugacity fraction values for all OPE homologs showed a declining trend. The distinct molecular structure of each OPE monomer might lead to unique diffusive behaviors at the sediment-water interface. Higher soot carbon content had a more pronounced effect on the distribution patterns of OPEs. The sediment-water distribution of OPEs was primarily influenced by total organic carbon, sediment particle size, dry density, and moisture content. OPEs displayed the highest sensitivity to fluctuations in ammonium and dissolved organic carbon. This study holds significant scientific and theoretical implications for elucidating the interfacial transport and driving forces of OPEs and comprehending their fate and endogenous release in aquatic ecosystems.

Flexible Ag-AgCl/TiO2/cellulose biocomposite film for solar photocatalytic degradation of VOCs

Photocatalyst immobilization on biopolymers presents a promising avenue for air purification, yet achieving efficient immobilization and purification methods remains a challenge. In this study, we develop a novel, straightforward sequential coating technique to fabricate cellulose film embedded with uniformly dispersed TiO2 submicrospheres and Ag-AgCl nanoparticles (NPs), tailored for effective indoor air purification under sunlight exposure. The obtained Ag-AgCl/TiO2/cellulose film exhibit remarkable photocatalytic prowess in degrading various volatile organic compounds (VOCs, including ethanol, 1-propanol, 1-butanol, propylamine, and propanethiol) under simulated sunlight, owing to the enhanced separation of charge carriers facilitated by the presence of plasmonic Ag-AgCl NPs. Notably, the nature of the functional groups and the carbon atom count within the VOCs molecular structures exert significant influence on the overall photocatalytic performance. Furthermore, our investigation into the reusability of this biocomposite film confirms its durability, particularly in mineralizing alcoholic compounds during VOC oxidization, and the deactivation primarily occurs with VOCs containing heteroatoms that can be residual on biocomposite surface. This study represents a significant step towards practical applications of biocomposite films with efficient photocatalytic activities for real-world air purification and environmental remediation.

Prediction of diffusion coefficients in aqueous systems by machine learning models

Currently there are no accurate models for the prediction of diffusion coefficients at infinite dilution in aqueous systems. Frequently, models that work well for polar solvents often perform worse in the case of water. At the same time, experimental data of tracer diffusion coefficients are scarce and can be impractical to measure when information on this important transport property is required. In this work, machine learning models were developed to predict the tracer diffusion coefficient of any solute in water at atmospheric pressure. Several approaches were carried out to construct the model, using different types of input parameters: pure component properties and theoretical molecular descriptors, such as atom counts, structural fragments and fingerprints, computed using different sources. A database of 126 systems (1192 data points) was used for training and the best model showed a global average absolute relative deviation (AARD) of 3.92%, with a maximum deviation of 24.27% on the test set. This model uses as inputs the temperature and 195 molecular descriptors computed using the RDKit cheminformatics package, which can be automatically calculated from a molecular identifier thus making the model very simple to use. In comparison, the well-known Wilke-Chang equation provided an AARD of 13.03% in the same test set, demonstrating the improved accuracy of the proposed solution. The models developed in this work are provided at github.com/EgiChem/ml-D12-water-app.

A Volcano Correlation between Catalytic Activity for Sulfur Reduction Reaction and Fe Atom Count in Metal Center.

Sulfur reduction reaction (SRR) facilitates up to 16 electrons, which endows lithium-sulfur (Li-S) batteries with a high energy density that is twice that of typical Li-ion batteries. However, its sluggish reaction kinetics render batteries with only a low capacity and cycling life, thus remaining the main challenge to practical Li-S batteries, which require efficient electrocatalysts of balanced atom utilization and site-specific requirements toward highly efficient SRR, calling for an in-depth understanding of the atomic structural sensitivity for the catalytic active sites. Herein, we manipulated the number of Fe atoms in iron assemblies, ranging from single Fe atom to diatomic and triatomic Fe atom groupings, all embedded within a carbon matrix. This led to the revelation of a "volcano peak" correlation between SRR catalytic activity and the count of Fe atoms at the active sites. Utilizing operando X-ray absorption and X-ray diffraction spectroscopies, we observed that polysulfide adsorption-desorption and electrochemical conversion kinetics varied up and down with the incremental addition of even a single iron atom to the catalyst's metal center. Our results demonstrate that the metal center with exactly two iron atoms represents the optimal configuration, maximizing atom utility and adeptly handling the conversion of varied intermediate sulfur species, rendering the Li-S battery with a high areal capacity of 23.8 mAh cm-2 at a high sulfur loading of 21.8 mg cm-2. Our results illuminate the pivotal balance between atom utilization and site-specific requirements for optimal electrocatalytic performance in SRR and diverse electrocatalytic reactions.

Investigating the catalytic activity of Mgn (n = 4–8) clusters for the hydrogen evolution reaction using density functional theory

AbstractTo efficiently desorb H2, pure Mgn (n = 4–8) clusters were chosen for the hydrogen evolution reaction with H2O. At the PBE0/def2‐TZVP level and the PBE0‐D3/def2‐TZVP level, the lowest energy structures of Mgn (n = 4–8) clusters and the most stable structures of Mgn@H2O (n = 4–8) complexes were searched in the local region. The transition state was predicted, and then the hydrogen evolution reaction channel was obtained by using the intrinsic reaction coordinate (IRC) to confirm the transition state. To better analyze the hydrogen reaction mechanism, the character of Mgn@H2O (n = 4–8) complexes and MgnO (n = 4–8) clusters, as well as the atomic charge change trend, were investigated using interaction region indicator function analysis (IRI) and natural population analysis (NPA). The reaction effect of Mg4 cluster and H2O is the worst. The energy barrier does, however, progressively lower as the cluster atom count rises, improving the reaction effect.

A statistical method for treating the “negative-age” and “negative-atom-number” conundrums in radioisotope dating

In radioisotope dating, statistical fluctuations of atom counts or decay counts can sometimes lead to unphysical results, such as negative counts after subtracting the background or negative apparent ages near the boundaries of the dating range. How to treat these boundary cases with a unified approach and give proper estimates on age limits and confidence intervals is an important issue in radioisotope dating. In this work, we combine the Feldman-Cousins likelihood-ratio ordering method for interval estimation, a Frequentist approach to make consistent transitions from the choice of one-sided limits to two-sided confidence intervals, and a Bayesian method to treat the background uncertainties. This data treatment method naturally eliminates the unphysical results. We use 81Kr dating and Atom Trap Trace Analysis as an example to illustrate the advantages. This method can be generalized to other radioisotopes and ultra-sensitive analysis techniques.

Network engineering of organosilica membranes for efficient pervaporation dehydration

In the petroleum industry, the mixing of ethanol (EtOH), isopropanol (IPA), and n-butanol (n-BuOH) with water is a prevalent occurrence in both production and recovery phases. Obtaining the required degree of purity via dehydration poses a significant challenge that needs to be addressed. Pervaporation has emerged as a promising method for separating azeotropic mixtures due to its energy efficiency and independence from vapor-liquid equilibrium constraints. In this study, organosilica precursors, namely 1,2-bis(triethoxysilyl)ethane (BTESE), 1,2-bis(triethoxysilyl)ethylene (BTESEthy), and 1,2-bis(triethoxysilyl)acetylene (BTESA), were utilized for the fabrication of organosilica membranes using the sol–gel method. Subsequently, these membranes were employed for pervaporation dehydration of EtOH, IPA, and n-BuOH. This study investigates the effect of the degree of unsaturation of the bridged group, feed composition, and C atom count of alcohol on membrane dehydration performance. The membranes demonstrated superior separation performance for the n-butanol/water mixtures compared to other alcohol/water systems investigated. In this particular context, BTESE membrane exhibited a significant separation factor, although accompanied by a reduced permeation flux. Conversely, BTESA membrane demonstrated a decreased separation factor but an increased permeation flux. The comparison between gas permeation and pervaporation highlighted that the separation mechanism was primarily governed by molecular sieving through the organosilica membranes.

Mathematical modeling of irregularity indices for adriamycin and their statistical analysis for emetic drugs

Anthracyclines exert cytotoxic effects on cancer cells through genotoxicity, inducing damage to their genetic material and impeding their reproductive capacity. The compound possesses a molecular framework consisting of an anthracycline ring system, which confers its pharmacological activity against cancer. Adriamycin is an anthracycline chemotherapeutic agent characterized by a structurally intricate chemical composition. In this article, we computed irregularity indices like the Albertson index, total irregularity index, Randić irregularity index and other degree-based irregularity indices to estimate the biological and chemical properties of Adriamycin. The findings are illustrated through the use of numerical figures and graphical representations. Further, we proposed the relationship between irregularity indices and the physicochemical properties of emetic drugs. We examined that the irregularity indices showed a statistically significant relationship with Molecular Weight, Monoisotopic Mass, Polar Surface Area, Heavy Atom Count, Complexity, Boiling Point, Enthalpy of Vaporization, Flash Point, Molar Refractivity and Polarizability based on the p-value (<0.5). We proposed multiple linear regression models that will be helpful to estimate the physical properties of those drugs that were not calculated. The numerical results and visual representations of our analyses will be helpful in the advancement of knowledge regarding the structure-property correlations of these drugs.

Element specific atom counting for heterogeneous nanostructures: Combining multiple ADF STEM images for simultaneous thickness and composition determination

In this paper, a methodology is presented to count the number of atoms in heterogeneous nanoparticles based on the combination of multiple annular dark field scanning transmission electron microscopy (ADF STEM) images. The different non-overlapping annular detector collection regions are selected based on the principles of optimal statistical experiment design for the atom-counting problem. To count the number of atoms, the total intensities of scattered electrons for each atomic column, the so-called scattering cross-sections, are simultaneously compared with simulated library values for the different detector regions by minimising the squared differences. The performance of the method is evaluated for simulated Ni@Pt and Au@Ag core–shell nanoparticles. Our approach turns out to be a dose efficient alternative for the investigation of beam-sensitive heterogeneous materials as compared to the combination of ADF STEM and energy dispersive X-ray spectroscopy.

Quantifying defects in graphene oxide structures

Oxidation of graphite and subsequent exfoliation leads to single layer graphene oxide (GO). One of the key structural features of GO is the presence of different kinds of defects that dictates their various physical and chemical properties. Molecular dynamics simulations with ReaxFF force fields have been widely used to generate realistic models of GO. In these simulations, the extent and distribution of the defects are varied by changing the initial oxygen (O)/carbon (C) ratio while the defect density is often measured by the total number of non-graphitic carbon (non-gC) atoms. Our calculations suggest that this parameter overestimates the defect densities at low O/C ratio. Herein, we employ the relative area of the defects as an alternative metric to gauge the defect density. Being exclusive to the defects and sensitive to the structural irregularities, this metric works well at both low and high defect densities. In another example, we consider the reduction of GO to reduced graphene oxide (rGO) for different O/C ratios, where the decrease in the number of non-gC atoms is associated with the formation of comparatively larger defects. Hence, it is unclear whether the extent of reduction in the defect density (if at all) should vary monotonically with the O/C ratio. The defect area, unlike the count of the non-gC atoms, mirrors this ambiguity and the change with respect to O/C ratio is not strictly monotonic. Additionally, we also investigate the dependence of the defect distribution and defect area on the size of the simulation cell.

In-silico Analysis of Adenosine Deaminase: From Gene Sequence Retrieval to Clinical Implications and Drug Targets

Background: Adenosine Deaminase (ADA) plays a critical role in purine metabolism and immune function. Its genetic and molecular characterization is essential for understanding its involvement in various physiological and pathological processes. The ADA gene has been implicated in immune system disorders and offers a potential target for therapeutic intervention. Objective: This study aims to comprehensively analyze the ADA gene and its protein product, detailing the genetic structure, physiochemical properties, subcellular localization, expression patterns, clinical variations, functional domains, splice variants, and potential as a drug target, to enhance the understanding of its function in human health. Methods: We utilized an array of in silico tools for gene sequence retrieval, including analysis of genomic databases for ADA's chromosomal location and gene structure. Physiochemical properties were determined using ProtParam, and subcellular localization was predicted with CELLO v.2.5. Gene expression levels across various tissues were ascertained using the Archive Ensemble TOOL. Clinical variations were categorized based on their potential pathogenicity, while splice variants and functional domains were predicted using domain-specific databases. Post-translational modifications were analyzed with prediction tools from the CBS server, and the STRING database facilitated protein interaction network analysis. Additionally, the DoG-Site Scorer predicted ADA's druggability profile. Results: ADA was localized to chromosome 12 (20q13.12), comprising 45940 base pairs encoding 363 amino acids. The protein's molecular weight was calculated as 37948.09, with a total atom count of 5396. Predictive localization indicated periplasmic and cytoplasmic presence with reliability scores of 1.437*, 1.210*, and 1.058*. Expression profiling revealed high ADA levels in the duodenum, with a notable expression in the lymphatic node. Clinical variations identified included 21 insertions and 23 inversions, with two pathogenic instances. Splice variant analysis predicted nine sites, and drug-binding potential was identified with scores as high as 0.81. Conclusion: Our study provides a detailed analysis of ADA, highlighting its genetic complexity and biological significance. The identification of clinically relevant gene variations and drug-binding pockets offers potential pathways for therapeutic intervention, emphasizing the importance of ADA in medical research and treatment strategies.

Every atom counts: predicting sites of reaction based on chemistry within two bonds

How much chemistry can be described by looking only at each atom, its neighbours and its next-nearest neighbours?