Unique Configurations Research Articles

Precise Synthesis of Single Atom Catalysts for Boosting Next‐Generation Advanced Oxidation and Reduction Processes in Sustainable Energy Applications

Single‐atom catalysts (SACs) demonstrate high selectivity, maximal atom utilization, and unique active site configurations, establishing them as a rapidly expanding research field. Understanding the intrinsic relationship between structure and catalytic performance is crucial for the effective use of SACs in catalysis. However, providing a clear explanation of the coordination environment and intrinsic structural regulation of SACs remains a significant challenge for next‐generation renewable energy materials, especially in advanced oxidation and reduction processes critical for sustainable energy applications. This comprehensive review offers an in‐depth overview of the current progress and design of SACs, with a specific focus on precise synthesis, structural control, and the relationship between structure and performance. Furthermore, we elucidate the reaction mechanisms of various catalytic systems and the selective methods used to precisely synthesize and enhance catalytic reactions in the sustainable energy sector. Finally, this review explores the complex challenges in investigating and developing SACs and offers a perspective on solutions in advanced oxidation and reduction technologies for future research to overcome these challenges and achieve practical applications.

Machine Learnable Language for the Chemical Space of Nanopores Enables Structure-Property Relationships in Nanoporous 2D Materials.

The synthesis of nanoporous two-dimensional (2D) materials has revolutionized fields such as membrane separations, DNA sequencing, and osmotic power harvesting. Nanopores in 2D materials significantly modulate their optoelectronic, magnetic, and barrier properties. However, the large number of possible nanopore isomers makes their study onerous, while the lack of machine-learnable representations stymies progress toward structure-property relationships. Here, we develop a language for nanopores in 2D materials, called STring Representation Of Nanopore Geometry (STRONG), that opens the field of 2D nanopore informatics. We show that STRONGs are naturally suited for machine learning via recurrent neural networks, predicting formation energies/times of arbitrary nanopores and transport barriers for CO2, N2, and O2 gas molecules, enabling structure-property relationships. The machine learning models enable the discovery of specific nanopore topologies to separate CO2/N2, O2/CO2, and O2/N2 gas mixtures with high selectivity ratios. We also enable the rapid enumeration of unique configurations of stable, functionalized nanopores in 2D materials via STRONGs, allowing systematic searching of the vast chemical space of nanopores. Using the STRONGs approach, we find that a mix of hydrogen and quinone functionalization results in the most stable functionalized nanopore configuration in graphene, a discovery made feasible by expedited chemical space exploration. Additionally, we also unravel the STRONGs approach as ∼1000 times faster than graph theory algorithms to distinguish nanopore shapes. These advances in the language-based representation of 2D nanopores will accelerate the tailored design of nanoporous materials.

Optimization of Electrodialysis for Ammonium Removal From NH4Cl-Doped Groundwater Samples Using the Response Surface Method.

This study aims to optimize ammonium removal from NH4Cl-enriched groundwater at different concentrations using an electrodialysis (ED) process. A customized design (CD) based on response surface methodology (RSM) was employed to develop predictive models and improve the performance of the demineralization system. Ion removal efficiency was evaluated in 32 unique experimental configurations, taking into account variations in three input parameters: voltage (A), initial ammonium concentration (B) and demineralization rate (C). These parameters were selected for their impact on two response variables: electric conductivity (Y1) and final ammonium concentration (Y2). An in-depth analysis of variance (ANOVA) was performed to examine the variables and their interactions. The results indicated that Y1 was significantly influenced by C, while Y2 was influenced by B. In addition, the predictive models demonstrated strong correlations, with a coefficient of determination (R2) greater than 0.88 for both response variables. The RSM approach applied to optimize the parameters studied identified the following optimum values: 14.17 V for A, 1 mg/L for B and 70 % for C, giving Y1 of 215.377 μS/cm and Y2 of 0.279 mg/L.

Analysing the feasibility of agro-based value chain configurations in the rural context with B2B marketing

Purpose This paper aims to advocate for small-scale entrepreneurship with commercial cultivation to earn better profits at the farmer level. This paper explores potential value chain configurations of vertical coordination. Design/methodology/approach The aloe vera supply chain was examined for the study where aloe vera juice is the product. The various configurations are complete vertical coordination, semi-vertical coordination and non-vertical coordination. These were analysed to find feasibility and market prospects based on parameters. The cyclic view of supply chains, comparative analysis and fuzzy-analytical hierarchy process algorithm are presented. Findings The semi-vertically coordinated supply chain is indicated as the optimum setup to initiate value chains. The entrepreneurs in this configuration will be able to sell semi-finished produce to a brand that will process and distribute the final product. This B2B setup will help the farmer to earn better revenues in the post-COVID-19 scenario. Effective marketing with improved execution and innovation will boost the food processing industry. Research limitations/implications This study uses an established methodology to distinguish among alternatives based on underlying factors. The study is based on data collected from stakeholders. The study reflects the most accurate picture of real-world scenarios at the farm and market level in the Indian context. Practical implications This study’s learnings will help understand the organisational approach to serve demand by designing unique business configurations. Adapting the fundamental model with technological intervention will improve supply chain performance and sustainability. The most modern approaches in agri-business, such as subscription models, vertical farming, corporate entrepreneurship and technological intervention, will be covered in future work. Social implications This study will help stakeholders, including the government, decide whether to introduce training skills, subsidy policies, business expansion ideas and feasibility studies. Originality/value The study highlights the strategic role of developing the B2B platform to support customer values and customer ecosystem with better product availability.

Integrative multi-omic and machine learning approach for prognostic stratification and therapeutic targeting in lung squamous cell carcinoma.

The proliferation, metastasis, and drug resistance of cancer cells pose significant challenges to the treatment of lung squamous cell carcinoma (LUSC). However, there is a lack of optimal predictive models that can accurately forecast patient prognosis and guide the selection of targeted therapies. The extensive multi-omic data obtained from multi-level molecular biology provides a unique perspective for understanding the underlying biological characteristics of cancer, offering potential prognostic indicators and drug sensitivity biomarkers for LUSC patients. We integrated diverse datasets encompassing gene expression, DNA methylation, genomic mutations, and clinical data from LUSC patients to achieve consensus clustering using a suite of 10 multi-omics integration algorithms. Subsequently, we employed 10 commonly used machine learning algorithms, combining them into 101 unique configurations to design an optimal performing model. We then explored the characteristics of high- and low-risk LUSC patient groups in terms of the tumor microenvironment and response to immunotherapy, ultimately validating the functional roles of the model genes through in vitro experiments. Through the application of 10 clustering algorithms, we identified two prognostically relevant subtypes, with CS1 exhibiting a more favorable prognosis. We then constructed a subtype-specific machine learning model, LUSC multi-omics signature (LMS) based on seven key hub genes. Compared to previously published LUSC biomarkers, our LMS score demonstrated superior predictive performance. Patients with lower LMS scores had higher overall survival rates and better responses to immunotherapy. Notably, the high LMS group was more inclined toward "cold" tumors, characterized by immune suppression and exclusion, but drugs like dasatinib may represent promising therapeutic options for these patients. Notably, we also validated the model gene SERPINB13 through cell experiments, confirming its role as a potential oncogene influencing the progression of LUSC and as a promising therapeutic target. Our research provides new insights into refining the molecular classification of LUSC and further optimizing immunotherapy strategies.

Urban Morphology Classification and Organizational Patterns: A Multidimensional Numerical Analysis of Heping District, Shenyang City

Prior studies have failed to adequately address intangible characteristics and lacked a comprehensive quantification of cultural dimensions. Additionally, such works have not merged supervised and unsupervised classification methodologies. To address these gaps, this study employed multidimensional numerical techniques for precise spatial pattern recognition and urban morphology classification at the block scale. By examining building density, mean floor numbers, functional compositions, and street block mixed-use intensities, alongside historical and contemporary cultural assets within blocks—with assigned weights and entropy calculations from road networks, building vectors, and POI data—a hierarchical categorization of high, medium, and low groups was established. As a consequence, cluster analysis revealed seven distinctive morphology classifications within the studied area, each with unique spatial configurations and evolutionary tendencies. Key findings include the dominance of high-density, mixed-use blocks in the urban core, the persistence of historical morphologies in certain areas, and the emergence of new, high-rise clusters in recently developed zones. The investigation further elucidated the spatial configurations and evolutionary tendencies of each morphology category. These insights lay the groundwork for forthcoming studies to devise morphology-specific management strategies, thereby advancing towards a more scientifically grounded, rational, and precision-focused approach to urban morphology governance.

Development of Higher-Level Vision: A Network Perspective.

Most studies on the development of the visual system have focused on the mechanisms shaping early visual stages up to the level of primary visual cortex (V1). Much less is known about the development of the stages after V1 that handle the higher visual functions fundamental to everyday life. The standard model for the maturation of these areas is that it occurs sequentially, according to the positions of areas in the adult hierarchy. Yet, the existing literature reviewed here paints a different picture, one in which the adult configuration emerges through a sequence of unique network configurations that are not mere partial versions of the adult hierarchy. In addition to studying higher visual development per se to fill major gaps in knowledge, it will be crucial to adopt a network-level perspective in future investigations to unravel normal developmental mechanisms, identify vulnerabilities to developmental disorders, and eventually devise treatments for these disorders.

Hydrodynamics of liquid–liquid parallel flow in novel microextractors: Review

Parallel flows on microfluidic platforms enable continuous liquid–liquid operations and inline separation of effluent streams, bearing immense scope in integration of miniaturized separation processes. However, these flows face major challenges including low mass transfer efficiency due to lack of transverse convection and flow instability at low flow rates, which undermine their operating range and utility. The limitations have inspired dedicated research, delving into the fundamentals of fluid flow and transport mechanism and exploring novel configurations of microextractors. The current article summarizes the hydrodynamics of parallel flows and relevant process intensification strategies in microfluidic extractors, evolving from the use of straight to curved and helical geometries, besides elucidating unique secondary flow patterns observed in-state-of-the-art designs. It includes exclusive sections addressing various aspects of parallel flows: (i) flow inception and theoretical modeling of flow fields and phase hold up, (ii) challenges concerning interfacial stability and flow intensification, (iii) curvature effects in planar curved geometries, and (iv) curvature cum torsional effects in unique multi-helical configurations. The theoretical perspective of this review presents a roadmap that can provide further insights into design modifications for developing improved integrated microextractors based on parallel flows.

SWiLoc: Fusing Smartphone Sensors and WiFi CSI for Accurate Indoor Localization.

Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone's direction and the user's actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc (Smartphone and WiFi-based Localization), an enhanced direction correction system that integrates passive WiFi sensing with smartphone-based sensing to form Correction Zones. Our two-phase approach accurately measures the user's walking directions when passing through a Correction Zone and further refines successive direction estimates outside the zones, enabling continuous and reliable tracking. In addition to direction correction, SWiLoc extends its capabilities by incorporating a localization technique that leverages corrected directions to achieve precise user localization. This extension significantly enhances the system's applicability for high-accuracy localization tasks. Additionally, our innovative Fresnel zone-based approach, which utilizes unique hardware configurations and a fundamental geometric model, ensures accurate and robust direction estimation, even in scenarios with unreliable walking directions. We evaluate SWiLoc across two real-world environments, assessing its performance under varying conditions such as environmental changes, phone orientations, walking directions, and distances. Our comprehensive experiments demonstrate that SWiLoc achieves an average 75th percentile error of 8.89 degrees in walking direction estimation and an 80th percentile error of 1.12 m in location estimation. These figures represent reductions of 64% and 49%, respectively for direction and location estimation error, over existing state-of-the-art approaches.

Advances and challenges in friction pendulum bearings for seismic isolation systems

Friction pendulum bearings (FPBs) have emerged as critical structural isolation devices in the field of earthquake engineering. Extensive research and development efforts have led to the exploration and refinement of these bearings, resulting in the creation of various models with unique structural configurations and superior mechanical properties. This paper offers an in-depth review of the friction materials employed in FPBs, alongside an analysis of the mechanical performance and advancements in research regarding single-pendulum, double-pendulum, multiple-pendulum, and other types of friction bearings. Characterized by their low sensitivity and high stability, these bearings are adept at resisting seismic forces, thus providing dependable isolation protection for structures. This review also includes a succinct examination of the seismic performance and engineering applications of these isolation structures. With robust self-centering capabilities, excellent isolation, and energy dissipation mechanisms, FPBs are capable of quickly resuming normal operations post-seismic events. This reduces structural damage and maintenance expenses, significantly improving the seismic resilience of structures. Moreover, the paper outlines the current challenges in the research and development of FPBs and suggests future research directions, including optimizing friction materials, enhancing the design and performance of isolation structures, and improving the seismic performance and engineering application efficiency of FPBs. By identifying underexplored areas and synthesizing findings differently, this review provides a comprehensive and novel perspective that advances the field of earthquake engineering.

Genetic Risk for Alcohol Use Disorder in Relation to Individual Symptom Criteria: Do Polygenic Indices Provide Unique Information for Understanding Severity and Heterogeneity?

Alcohol Use Disorder (AUD) is a heterogenous category with many unique configurations of symptoms. Previous investigations of AUD heterogeneity using molecular genetics methods studied the association between genetic liability and individual AUD symptoms at the latent level or focusing on a small number of genetic variants. Notably, these studies did not investigate potential severity differences between symptoms in their genetic analyses. Therefore, the current study aimed to examine the genetic risk for individual AUD symptom criteria by using a polygenic risk score (PRS) approach to assess the relative severity of each AUD symptom and test for associates with AUD symptoms above and beyond a unidimensional AUD construct. An AUD PRS was created using summary statistics obtained from published genome-wide association studies (GWAS), and Multiple Indicators Multiple Causes (MIMIC) models were employed to examine the effect of the PRS on overall AUD severity as well as on individual symptoms after accounting for this overall effect. The phenotypic severity of AUD symptoms was highly correlated with the genetic severity of AUD symptoms (r = 0.78). Results of MIMIC models indicated that the AUD PRS significantly predicted the AUD factor. Regression paths testing the unique, direct effects of the PRS on individual AUD symptoms, independent of the latent AUD factor, were not significant. These results imply that PRSs derived from GWAS of AUD influence symptom expression through a single genetic factor that is highly correlated with the relative severity of individual symptoms when measured at the phenotypic level. Item-level GWAS of AUD symptoms are needed to further parse heterogeneous symptom expression and allow for more nuanced tests of these conclusions.

Sulfate salt assistant fabrication of Fe-doped Ni2P modified with SO42−/carbon as highly efficient oxygen evolution reaction electrocatalyst

The incorporation of oxyanion groups offers a greater potential for enhancing the activity of oxygen evolution reaction (OER) electrocatalysts compared to traditional metal cations doping, owing to their unique configurations and high electronegativity. However, the incorporation of oxyanion groups that differ from those derived from the oxidation of anions in transition metal monoxides poses significant challenges, thereby limiting further applications of oxyanion group modification approach. Herein, we present a novel sulfate salt assistant approach to fabricate Fe-doped Ni2P modified with SO42−/carbon (Fe-Ni2P-S/C) nanofibers as highly efficient OER electrocatalyst. The optimized Fe-Ni2P-S/C nanofibers display superb OER activity, requiring low overpotentials of 266, 323, and 357 mV at 100, 500, and 1000 mA cm−2, respectively. Theoretical calculations reveal that the co-adsorption of PO43− and SO42− on the surface of reconstructed electrocatalyst can reduce the energy barrier of rate-determining step, thereby resulting in enhanced OER activity. The present study emphasizes the crucial role played by anion groups in OER activity as well as proposes a novel approach for incorporating anion groups into electrocatalysts.

Copper(I) Complexes With Phenanthroline‐Functionalized NHC Ligands Achieve Effective Anticancer Activity via Mitochondrial and Endoplasmic Reticulum Stress

ABSTRACTCopper complexes exhibit promising anticancer properties, which can be further promoted by suitable ligands. In this study, we synthesized and investigated the anticancer effects of three copper(I) complexes with N‐heterocyclic carbene (NHC) ligands derived from phenanthroline‐functionalized imidazole salts. The synthesis of these NHC‐copper(I) complexes (Cu1‐Cu3) was meticulously characterized using NMR and elemental analysis; their crystal structures were further delineated by X‐ray diffraction. Our findings illustrate the unique geometric configurations of these complexes: Cu1 features a dinuclear arrangement with off‐linear geometry; Cu2 and Cu3 are characterized by a bi‐copper core stabilized by two phenanthroline‐modified carbene ligands. Crucially, in vitro cytotoxic evaluations revealed that these complexes possess suitability anticancer activity. Among them, Cu2 exhibited enhanced cytotoxicity activity, achieving a half‐maximal inhibitory concentration (IC50) of 4.36 ± 0.25 μM in colon cancer cell line HCT‐116, markedly surpassing the efficacy of cisplatin. Subsequent investigations elucidated that Cu2 notably increases intracellular reactive oxygen species (ROS) levels and induces mitochondrial membrane depolarization and endoplasmic reticulum stress, leading to oxidative stress‐mediated apoptosis of colon cancer cells.

Techno-economic analysis of a hybrid energy system for electrification using an off-grid solar/biogas/battery system employing HOMER: A case study in Vietnam

The electrification of off-grid /island villages is a critical step towards improving the techno-economic circumstances of rural regions and the overall general growth of the country. However, consistent supply from a single source is not possible in these areas. Thus, a hybrid renewable energy system performs better in these conditions. The research challenge now is to identify the optimal combinations of HRES from the available resources in a specific village site that can supply the power demand sustainably and to determine whether this is a cost-effective option. The present work is an endeavour to develop a sustainable and dynamic energy demand-supply model using HOMER Pro energy software in a specified off-grid rural site in Vietnam. The research presents four unique configurations of a combined energy system for Vietnam's island settlements, incorporating biomass-based biogas facilities, photovoltaic panels, lithium-ion batteries, and converters. Homer Pro was used for optimization and design, focusing on key performance measures such as cost of energy, net present cost, initial cost, operating cost, renewable fraction, and carbon emissions. The best HES system layout includes a 100-kW biomass-based generator, 2.62 kW photovoltaic installation, 10 lithium-ion batteries, and a 6.31 kW converter, producing 100 % renewable energy. The system's low cost of energy ($0.48), and net present cost ($25,730.89) make it an economically viable alternative, while its low CO2 emissions demonstrate its commitment to reducing greenhouse gas emissions.

Seeding Co Atoms on Size Effect‐Enabled V2C MXene for Kinetically Boosted Lithium–Sulfur Batteries

AbstractLithium‐sulfur (Li–S) batteries are facing a multitude of challenges, mainly pertaining to the sluggish sulfur redox kinetics and rampant lithium dendrite growth on the cathode and anode side, respectively. In this sense, MXene has shown conspicuous advantages in serving as a dual‐functional promotor for Li–S batteries throughout the morphologic engineering, but still suffers from poor electrocatalytic activity and insufficient lithophilic sites. Herein, atomically dispersed Co sites are seeded onto the size effect‐enabled V2C MXene spheres (Co‐VC), leading to the generation of unique coordination configurations and rich active sites. Electrochemical tests combined with synchrotron radiation X‐ray 3D nano‐computed tomography and theoretical calculations unravel that Co‐VC with optimal coordination environments simultaneously boost sulfur reaction kinetics and lithium nucleation. As a consequence, Li–S batteries with Co‐VC modified separator can sustain a stable operation over 700 cycles with negligible capacity decay at 1.0 C, and delivers an areal capacity of 9.0 mAh cm−2 and desired cyclic performance at a high sulfur loading of 7.6 mg cm−2 with a lean electrolyte dosage of 4.0 µL mgS−1 at 0.1 C. The work opens a new avenue for boosting atomic‐scale site design with the aid of 2D substrates toward pragmatic Li–S batteries.

Safety-driven design of carbon capture utilization and storage (CCUS) supply chains: A multi-objective optimization approach

Carbon capture, utilization, and storage supply chains (CCUS) play a pivotal role in achieving sustainability targets but necessitate meticulous risk identification and mitigation measures. Traditional safety assessments often occur post-design, constraining proactive risk management efforts. Hence, there is a pressing need to optimize safety performance during the design stages. To address this challenge, a framework for evaluating and optimizing CCUS supply chain safety performance using inherent safety index system (ISI) is introduced. Recognizing the trade-offs between total cost, environmental impact reduction, and risk mitigation, our approach considers multi-objective optimization to concurrently address these sustainability objectives and generate a Pareto set of solutions. Utilizing the augmented ε-constraint method, we applied this framework to optimize CCUS networks and develop sustainable designs across three key objectives. The method was applied to a CCUS system that includes various CO2 utilization pathways to minimize the total annual cost, CO2 emissions, and safety risks. The resulting Pareto surface illustrates unique network configurations, each representing a distinct trade-off scenario. Through a case study, we optimized a CCUS network to achieve economic, environmental, and safety objectives. The most economically viable design, with a total annual cost of $97 million and a 40 % net carbon reduction, prioritizes CO2 utilization for value-added products, while limiting CO2 sequestration. Conversely, safety-focused designs shift utilization towards safer routes, including CO2 sequestration and algae production. The proposed framework offers a systematic approach to developing sustainable CCUS supply chain designs, balancing economic viability, environmental sustainability, and safety.

Task-specific topology of brain networks supporting working memory and inhibition.

Network neuroscience explores the brain's connectome, demonstrating that dynamic neural networks support cognitive functions. This study investigates how distinct cognitive abilities-working memory and cognitive inhibitory control-are supported by unique brain network configurations constructed by estimating whole-brain networks using mutual information. The study involved 195 participants who completed the Sternberg Item Recognition task and Flanker tasks while undergoing electroencephalography recording. A mixed-effects linear model analyzed the influence of network metrics on cognitive performance, considering individual differences and task-specific dynamics. The findings indicate that working memory and cognitive inhibitory control are associated with different network attributes, with working memory relying on distributed networks and cognitive inhibitory control on more segregated ones. Our analysis suggests that both strong and weak connections contribute to cognitive processes, with weak connections potentially leading to a more stable and support networks of memory and cognitive inhibitory control. The findings indirectly support the network neuroscience theory of intelligence, suggesting different functional topology of networks inherent to various cognitive functions. Nevertheless, we propose that understanding individual variations in cognitive abilities requires recognizing both shared and unique processes within the brain's network dynamics.

Theta-curves in proteins.

We study and characterize the topology of connectivity circuits observed in natively folded protein structures whose coordinates are deposited in the Protein Data Bank (PDB). Polypeptide chains of some proteins naturally fold into unique knotted configurations. Another kind of nontrivial topology of polypeptide chains is observed when, in addition to covalent bonds connecting consecutive amino acids in polypeptide chains, one also considers disulfide and ionic bonds between non-consecutive amino acids. Bonds between non-consecutive amino acids introduce bifurcation points into connectivity circuits defined by bonds between consecutive and nonconsecutive amino acids in analyzed proteins. Circuits with bifurcation points can form θ-curves with various topologies. We catalog here the observed topologies of θ-curves passing through bridges between consecutive and non-consecutive amino acids in studied proteins.

All-Calixarene-Protected Silver Nanocluster with All Silver Atoms in a Face-Centered Cubic Arrangement.

Tailoring the surface ligands of metal nanoclusters is important for engineering unique configurations of metal nanoclusters. Thiacalix[4]arene has found extensive applications in the construction of metal nanoclusters. In this investigation, we present the synthesis and characterization of the first all-calixarene-protected silver nanoclusters, [Ag(CH3CN)4]2[Ag44(BTCA)6] (Ag44, H4BTCA = p-tert-butylthiacalix[4]arene). Single-crystal X-ray structural analysis reveals that all silver atoms are in a face-centered cubic (fcc) arrangement. The formation of such an fcc structure is attributed to the selectively passivation on {100} facets by BTCA4-. Thiacalixarene substantially facilitates the stability of Ag44 due to its multiple coordination sites and bulkiness. Mass spectrometry and theoretical calculations reveal that Ag44 is a superatomic silver nanocluster with 22 free electrons in the following configuration: 1S21P61D61F22S21D4. This work not only elucidates the impact of macrocyclic ligands on the stabilization of silver clusters but also furnishes an approach for assembling atomically precise fcc nanoclusters.

Clockwise vs anti-clockwise chiral metamaterials: Influence of base tessellation symmetry and rotational direction of chiralisation on mechanical properties

Chiral honeycombs are a class of auxetic metamaterials which are characterised by a lack of plane symmetry. Besides the traditional chiral honeycombs based on regular monohedral tessellations such as the hexachiral and tetrachiral honeycombs, a vast range of auxetic chiral metamaterials may be produced through the ‘chiralisation’ of Euclidean tessellations made from polyhedral tilings and/or irregular monohedral polygons. In this work, we show for the first time, how the direction of the geometric chiral transformation, i.e. clockwise vs anti-clockwise chiralisation, has an influence on the mechanical properties and deformation modes of the resultant chiral metamaterial systems. This influence, which is a result of the intrinsic absence of axial symmetry in the original base tessellation, can lead to the production of two unique and distinct chiral metamaterial configurations with completely different mechanical properties originating from a single base tessellation. In this work we demonstrate and quantify, through a wide range of numerical simulations and experimental tests on additively-manufactured prototypes, the effect of the direction of chiralisation on two tessellations: a Florent pentagonal system with hexagonal rotational symmetry and a hexagonal monohedral tessellation with trigonal rotational symmetry. The results obtained show that the clockwise and anti-clockwise chiral structures exhibit significantly different mechanical properties and deformation modes, highlighting the increased versatility of this relatively novel class of chiral metamaterials.