Background/Objectives: Accurate full-arch impressions are crucial for predictable prosthodontic outcomes. While intraoral scanners (IOSs) are increasingly adopted, evidence comparing their accuracy with conventional analog impressions across full mandibular arches—particularly under both laboratory and clinical conditions using an objective intraoral reference—is limited. Our study aims to evaluate the in vitro and in vivo accuracy of digital impressions compared to conventional methods in full-arch scans using an intraoral reference tool. Methods: In this study, a custom stainless steel transfer aid carrying four 5 mm steel spheres in a trapezoidal configuration, provided with known reference distances, was used. Ten mandibular Frasaco models (in vitro) and ten healthy young adults (18–30 yrs) with intact lower arches (in vivo) received the bonded spheres. Six inter-sphere distances were defined: intermolar (BL-BR), interpremolar (FL-FR), diagonals (BL-FR, FL-BR), and lateral spans (BL-FL, BR-FR). Each arch underwent a digital scan (Medit i700) and a conventional monophase PVS impression, which was poured in Type IV stone and digitized (GOM Scan 1). The inter-sphere linear distances were measured in GOM Inspect, and trueness (deviation from reference) and precision (SD) were calculated. Data normality and homogeneity were verified; parametric t-tests and one-sample tests (α = 0.05) assessed differences between workflows and against reference values. Results: In vitro, analog impressions closely matched reference distances, with only the long-span BL-BR showing minor deviation (0.053 mm, p < 0.001). Digital scans showed significantly greater deviations across all spans (max 0.117 mm), particularly over long distances. In vivo, both workflows demonstrated comparable accuracy: only BL-BR (analog) and BR-FR (digital) differed significantly from the reference, and all AMEs remained within clinical thresholds (≤0.10 mm), except for BL-BR and BL-FL spans. ICC values ranged from moderate to high. Direct paired comparisons revealed statistically equivalent performance across most spans. Conclusions: Analog impressions outperformed digital scans in vitro, particularly across longer spans, confirming their superior dimensional fidelity under controlled conditions. However, in vivo, both workflows delivered statistically comparable and clinically acceptable accuracy. These findings suggest that while analog impressions remain the gold standard for precision-demanding contexts, modern intraoral scanners—when used correctly—can offer reliable full-arch mandibular impressions. The four-sphere reference system proved valuable for objective, anatomy-independent measurement.

Oncogenic Ras proteins are known to present multiple conformational states, as reported by the great variety of crystallographic structures. The GTP-bound states are grouped into two main states: the “inactive” state 1 and the “active” state 2. Recent reports on H-Ras have shown that state 2 exhibits two substates, directly related to the orientation of Tyr32: toward the GTP-bound pocket and outwards. In this paper, we show that N-Ras exhibits another substate of state 2, related to a third orientation of Tyr32, toward Ala18 and parallel to the GTP-bound pocket. We also show that this substate is highly sampled in the G12V mutation of N-Ras and barely present in its wild-type form, and that the G12V mutation prohibits the sampling of the GTPase-activating protein (GAP) binding substate, rendering this mutation oncogenic. Furthermore, using molecular dynamics simulations, we explore the importance of the membrane on N-Ras’ conformational state dynamics and its strong influence on Ras protein stability. Moreover, the membrane has a significant influence on the conformational (sub)states sampling of Ras. This, in turn, is of crucial importance in the activation/deactivation cycle of Ras, due to the binding of guanine nucleotide exchange factor proteins (GEFs)/GTPase-activating proteins (GAPs).

The present work delineates the tailoring of the thermal effusivity and diffusivity of the novel class of heat transfer fluids—ionanofluids—by the incorporation of nanocarbons like diesel soot (DS), camphor soot (CS), carbon nanotubes (CN), and graphene (GR). When the thermal diffusivity delivers information on the thermal energy propagation, the thermal effusivity concerns the energy exchange at the interface, enabling energy-efficient thermal system design. The nanocarbons are subjected to morphological characterisation by field emission scanning electron microscopy. Fourier-transform infrared and Raman spectroscopic analyses confirm functional groups and vibrational bands. The microcrystalline size and graphiticity are also understood from the Raman spectrum. Ionanofluids prepared by dispersing nanocarbons into an ionic liquid base 1-Butyl-3-methylimidazolium methyl sulfate (BMMS) are analysed by nondestructive photopyroelectric calorimetry (PPE). The PPE analysis of ionanofluids demonstrates that nanocarbons influence thermal parameters in the base fluid, with soot ionanofluids exhibiting increased thermal effusivity and diffusivity due to their various carbon allotropic composition. This study underscores the importance of selecting the appropriate carbon allotrope for tailoring ionanofluids’ thermal properties, providing insights into manipulating these properties for enhanced performance across various industrial applications.

Lignin is one of the most abundant natural biopolymers and plays a crucial role in the development of safe and sustainable alternatives for healthcare products. In this study, we employed molecular dynamics simulations and free energy calculations to investigate lignin derivatives’ interactions with skin-like membranes. Specifically, we designed a small lignin derivative composed of syringyl and guaiacyl subunits. Our results reveal that molecular size, concentration, and thermal conditions critically influence the insertion, interaction dynamics, and localization behavior of lignin derivatives. Notably, variations in these parameters induce distinct behaviors, including rapid membrane insertion, hydrogen bonding, clustering, and surface adhesion. The findings provide insights into the molecular mechanisms governing lignin derivatives’ interactions with skin-like membranes, with implications for developing bio-based skincare formulations and transdermal delivery systems. Our results highlight the importance of molecular size and concentration in optimizing lignin-derived compounds for dermatological and therapeutic applications.

A general theory of a photopyroelectric (PPE) configuration, based on an opaque sample and transparent pyroelectric sensor, backing and coupling fluids is developed. A combined back-front detection investigation, based on a frequency scan of the phase of the PPE signals, followed by a self-normalization of the phases’ behavior, leads to the possibility of simultaneously measuring both thermal effusivity and diffusivity of a solid sample. A particular case of this configuration, with no coupling fluid at the sample/backing interface and air instead of coupling fluid at the sample/sensor interface (non-contact method) is suitable for simultaneous measurement ofboth thermal diffusivity and effusivity (in fact complete thermal characterization) of porous solids. Compared with the already proposed configurations for investigations of porous materials, this novel configuration makes use of a fitting procedure with only one fitting parameter, in order to guarantee the uniqueness of the solution. The porous solids belong to a class of materials which are by far not easy to be investigated using PPE. To the best of our knowledge, porous materials represent the only type of compounds, belonging to condensed matter, which were not taken into consideration (until recently) as potential samples for PPE calorimetric investigations. Consequently, the method proposed in this paper complete the area of applications of the PPE method. Applications on some porous building materials and cellulose-based samples validate the theory.

Polymers are highly versatile and cost-effective materials with a wide range of tunable physicochemical properties, making them indispensable across numerous technological and biomedical applications. However, their intrinsic chemical heterogeneity and complex structural organization pose significant challenges for the rational design of advanced systems, particularly in gene and pharmaceutical delivery. To address these limitations, the emerging field of polymer informatics leverages data-driven and computational approaches to enable efficient prediction of material properties and accelerate design processes through machine learning techniques. A data-driven polymer informatics framework is developed by integrating molecular dynamics simulations with machine learning models to enable rapid and accurate prediction of key structural descriptors, including the radius of gyration and solvent-accessible surface area. The models establish quantitative relationships between polymer chain length and the corresponding structural descriptors across a diverse range of polymer architectures. Recognizing that different properties may require different model configurations, property-specific neural network architectures are employed to enhance predictive performance. The resulting models are deployed within an accessible online platform, , designed to streamline polymer design workflows through real-time prediction and visualization. Ongoing developments focus on expanding the chemical space and incorporating additional properties, thereby increasing the platform’s applicability across scientific and technological domains.