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Probabilistic Risk Framework for Nuclear- and Fossil-Powered Vessels: Analyzing Casualty Event Severity and Sub-Causes
(MDPI, 2025) Tanyıldızı-Kökkülünk, Handan; Kökkülünk, Görkem; Settles, John
Maritime activities pose significant safety risks, particularly with the growing presence of nuclear-powered vessels (NPVs) alongside traditional fossil-powered vessels (FPVs). This study employs a probabilistic risk assessment (PRA) approach to evaluate and compare accident hazards involving NPVs and FPVs. By analyzing historical data from 1960 to 2024, this study identifies risk patterns, accident frequency (probability), and severity levels. The methodology focuses on incidents such as marine incidents, marine casualties, and very serious cases with sub-causes. Key findings reveal that Russia exhibits the highest risk for very serious incidents involving both NPVs and FPVs, with a significant 100% risk for NPVs. China has the highest FPV risk, while France and the USA show above-average risks, particularly for marine casualties and very serious incidents. Moreover, collision is the most significant global risk, with a 26% risk for NPVs and 34% for FPVs, followed by fire hazards, which also pose a major concern, with a 17% risk for NPVs and 16% for FPVs, highlighting the need for enhanced safety and fire-prevention measures. In conclusion, comparative analysis highlights the need for enhanced stability improvements, fire prevention, and maintenance practices, particularly in the UK, France, Russia, and China. This study underscores the importance of targeted safety measures to mitigate risks, improve ship design, and promote safer maritime operations for both nuclear- and fossil-fueled vessels.
A machine learning assisted designing and chemical space generation of benzophenone based organic semiconductors with low lying LUMO energies
(Elsevier Ltd, 2025) Güleryüz, Cihat; Hassan, Abrar U.; Güleryüz, Hasan; Kyhoiesh, Hussein A.K.; Mahmoud, Mohamed H.H.
Current study presents a machine learning (ML) approach to design benzophenone-based organic chromophore with their lowest possible LUMO energy (ELUMO). A dataset of their 1142 donors is collected from literature and their molecular descriptors are designed by using RDKit. Among various models, the Random Forest regression model produces accurate results to predict their ELUMO values. Based on these predictions, their 5000 new donors are designed with their Synthetic Accessibility Likelihood Index (SALI) scores. Their SHAP value analysis reveals that their electro topological state indices are the most critical descriptors to lowering ELUMOs. The top- performing donor are further extended with acceptors and their photovoltaic (PV) properties by density functional theory (DFT). Their results show their maximum open-circuit voltage (Voc) of 2.30 V, a short-circuit current (Jsc) of 47.19 mA/cm2, and a light-harvesting efficiency (LHE) of 93 %. This study demonstrates the potential of ML assisted design to design new organic chromophores.
A graph neural network assisted reverse polymers engineering to design low bandgap benzothiophene polymers for light harvesting applications
(Elsevier Ltd, 2025) Hassan, Abrar U.; Güleryüz, Cihat; El Azab, Islam H.; Elnaggar, Ashraf Y.; Mahmoud, Mohamed H.H.
In this study, we present a novel approach to reverse polymer engineering utilizing a Graph Neural Network (GNN) framework to design low bandgap benzothiophene (BT) polymers for light harvesting applications. We have curated an extensive dataset comprising 57,556 structure-property pairs of BT-based compounds, leveraging expert knowledge to enhance the quality and relevance of the data. Our Transformer-Assisted Oriented pretrained model for on-demand polymer generation (TAO) demonstrates exceptional performance, achieving a chemical validity rate of 99.27 % in top-1 generation mode across a test set of 6000 generated polymers, marking the highest success rate reported among polymer generative models to date. Throughout the training process, the loss steadily decreased with each epoch, indicating that the model was learning effectively from the data. The model predictive accuracy is further validated by an impressive average R2 value of 0.96 for 15 defined properties, highlighting the TAO with its robust capabilities in polymer design. The newly designed polymers exhibit a bandgap range of 1.5–3.40 eV, making them promising candidates for light harvesting applications. Additionally, their highest Synthetic Accessibility Likelihood Index (SALI) scores reach up to 17 and also indicates that the majority of these polymers are amenable to synthesis. This work not only advances the field of polymer design but also provides a powerful tool for the targeted development of materials with specific electronic properties.
A machine learning analysis to predict the stability driven structural correlations of selenium-based compounds as surface enhanced materials
(Elsevier Ltd, 2025) Güleryüz, Cihat; Sumrra, Sajjad H.; Hassan, Abrar U.; Mohyuddin, Ayesha; Elnaggar, Ashraf Y.; Noreen, Sadaf
The selenium-based compounds are gaining significance for their surface-enhanced properties. In order to accelerate their discovery, a machine learning (ML) approach has been employed to predict their structural correlations. For this a dataset of 618 compounds is collected from literature and is trained by using Support Vector Machine (SVM) with its Linear Kernal. Among ten ML evaluated models, three top-performing models are selected to make predictions for their stability energy. A Convex Hull Distribution (CHD) is constructed to elucidate the relationship for their stability and structural correlations. The main finding of this study reveals its strong correlation between stability and its related structural descriptors, particularly Bertz Branching Index" corrected for the number of Terminal atoms (BertzCT), Partial Equalization of Orbital Electronegativities-Van der Waals Surface Area with 14 bins (PEOE_VSA14), and First-Order Connectivity Index (chi 1). The analysis demon strates that the current ML models can effectively predict the stability of such materials to enable their rapid screening. Their calculations can provide a framework to understand their complex relationships between their material properties, structure, and stability.
Recent advances of structure, function, and engineering of carboxylesterases for the pharmaceutical industry: A minireview
(IPC Science and Technology Press, 2025) Sürmeli, Yusuf; Vardar-Yel, Nurcan; Tütüncü, Havva Esra
Carboxylesterases have a wide range of applications due to their catalytic efficiency, robust structure, and broad substrate specificity. These enzymes, which can hydrolyze carboxylic acid esters, amides, and thioesters, stand out with their regio- and enantioselective properties. They play a crucial role in synthesizing pharmaceutical intermediates, including secondary and tertiary alcohols, α-hydroxy acids, and various bioactive compounds. However, in some cases, the enantioselectivity of carboxylesterases may be insufficient to achieve conversions with the purity required by the pharmaceutical industry. This review summarizes the crucial role of carboxylesterases, particularly in the pharmaceutical field, focusing on the classification, structure, and engineering approaches. After introducing the main families of carboxylesterases, the structural studies are presented to give a comprehensive insight into the active site architecture and related key determinants for enantioselectivity. The protein engineering studies to improve the enantioselectivity of carboxylesterases are discussed along with solvent engineering and immobilization applications.
Dual-Functioning Metal-Organic Frameworks: Methotrexate-Loaded Gadolinium MOFs as Drug Carriers and Radiosensitizers
(2025) Karaca, Burcu; Sakarya, Deniz; Siyah, Pınar; Şenışık, Ahmet Murat; Kaptan, Yasemin; Çavuşoğlu, Ferda C.; Mansuroğlu, Demet S.; Öztürk, Sadullah; Bayazıt, Şahika S.; Barlas, Fırat
Cancer remains a critical global health challenge, necessitating advanced drug delivery systems through innovations in materials science and nanotechnology. This study evaluates gadolinium metal-organic frameworks (Gd-MOFs) as potential drug delivery systems for anticancer therapy, particularly when combined with radiotherapy. Gd-MOFs were synthesized using terephthalic acid and gadolinium (III) chloride hexahydrate and then loaded with methotrexate (MTX). Characterization via fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), magnetic resonance imaging (MRI), and X-ray diffraction (XRD) confirmed their correct structure and stability. Effective MTX loading and controlled release were demonstrated. Anticancer effects were assessed on human healthy bronchial epithelial cells (BEAS-2B) and human lung cancer cells (A549) using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay under in vitro radiation therapy. MTX/Gd-MOF combined with radiotherapy showed a greater reduction in cancer cell viability (41.89% ± 2.75 for A549) compared to healthy cells (56.80% ± 1.97 for BEAS-2B), indicating selective cytotoxicity. These findings highlight the potential of Gd-MOFs not only as drug delivery vehicles but also as radiosensitizers, enhancing radiotherapy efficacy and offering promising evidence for their use in combinatory cancer therapies to improve treatment outcomes.
Investigation of the tissue equivalence of typical 3D-printing materials for application in internal dosimetry using monte carlo simulations
(2025) Karadeniz-Yıldırım, Ayşe; Tanyıldızı-Kökkülünk, Handan
This study evaluates the dosimetric accuracy of PLA and ABS 3D-printed phantoms compared to real tissues using Monte Carlo simulations in radionuclide therapy. Materials and methods: A phantom representing average liver and lung volumes, with a 10 mm tumor mimic in the liver, was simulated for radioembolization using 1 mCi Tc-99 m and 1 mCi Y-90. The dose distribution (DD) was compared across PLA, ABS, and real organ densities. Results: For Tc-99 m, PLA showed a + 5.6% DD difference in the liver, and ABS showed - 35.3% and - 40.9% differences in the lungs. For Y-90, PLA had a + 1.7% DD difference in the liver, while ABS showed - 34.2% and - 34.9% differences in the lungs. Conclusion: In MC simulation, PLA is suitable for representing high-density tissues, while ABS is appropriate for simulating moderately low-density tissues.
A rapid UV/Vis assisted designing of benzodithiophene based polymers by machine learning to predict their light absorption for photovoltaics
(Elsevier B.V., 2025) Hassan, Abrar U.; Güleryüz, Cihat; Sumrra, Sajjad H.; Noreen, Sadaf; Mahmoud, Mohamed H.H.
As global energy demands escalate, developing high-performance photovoltaic (PV) materials through accelerated design methodologies is imperative. A machine learning (ML) assisted predictive models are used to accelerate the design of benzodithiophene (BDT)-based polymers for their PV applications. The current approach leverages a curated dataset of 191 compounds with experimental UV–Vis spectra, mapped to molecular electronic descriptors via RDKit. Random Forest modeling yields a predictive framework (R2 = 0.98) for predicting their maximum absorption (λmax). After it, their 5000 new designs as novel polymers, identifying top performers with Synthetic Accessibility Likelihood Index scores up to 57, ensuring synthesis feasibility have also been designed. Feature importance analysis highlights MaxPartialCharge and Aromatic rings as crucial descriptors. The designed materials exhibit optimal energy gaps (1.35–2.0 eV), paving the way for efficient PV devices. The computed UV–Vis spectra of best predicted polymers are studied with their λmax range of 487–987 nm showing a significant redshift behavior. The designed polymers presents and good potential towards and they can be good candidates for organic solar cell applications.
Exploring the structural basis of crystals that affect nonlinear optical responses: An experimental and machine learning quest
(Elsevier B.V., 2025) Hassan, Abrar U.; Güleryüz, Cihat; El Azab, Islam H.; Elnaggar, Ashraf Y.; Mahmoud, Mohamed H.H.
Machine learning can enable a computational framework to learn from data, thereby enhancing decision-making for targeted properties. Based on the significance of nonconjugated crystals as effective switches, an ML based approach has been applied to evaluate driving forces behind their polarizability/hyperpolarizability related hyper-Rayleigh Scattering (βHRS). For this, a dataset of relevant 1,3,5-triazine-2,4,6-triamine related structures in collected from peer reviewed literature to design its molecular descriptors. The designed dataset is trained on different regression models along with their cross-validation techniques include K-Fold and Leave One Group Out. It shows that Random Forest Regression can predict their polarizabilities with a fair accuracy (R2 = 0.83). Additionally, it shows its energy gaps (Egaps) ranging from 4.62 to 4.89 eV, with the smallest gap observed in ethanol. Understanding both these theoretical and experimental calculations can significantly help in selecting materials for targeted purposes, including sensors, electronic devices, and catalysis. Furthermore, insights into nucleophilic tendencies and charge distributions aids in designing new materials with tailored properties, expanding their use in various applications across chemistry, materials science, and other fields. The ML techniques prove its effectiveness to predict polarizabilities in response to its computational realm due to feature design, regression models with their cross-validations.
A fast and efficient machine learning assisted prediction of urea and its derivatives to screen crystal propensity with experimental validation
(Elsevier Ltd, 2025) Güleryüz, Cihat; Sumrra, Sajjad H.; Hassan, Abrar U.; Mohyuddin, Ayesha; Noreen, Sadaf; Elnaggar, Ashraf Y.
Predicting crystal propensity is crucial yet challenging in various industries where it significantly influences product stability, performance, and efficacy. Predicting a crystal propensity identifies their optimal chemical structures for desired properties including solubility, bioavailability, shelf-life stability etc. Herein, A machine learning (ML) assisted analysis is performed to predict their crystal propensity by collecting a dataset of 6000 non-crystalline and over 200 crystalline urea and its derivatives. The data is trained by employing a Support Vector Machine (SVM) with its Radial Basis Function (RBF) and linear kernels along with Random Forest regression analysis. The trained data is compared with four other ML models, including Linear Regression, Gradient Boosting, Random Forest and Decision Tree Regressions to predict their crystal propensity. It yields an accuracy of 79 % for identifying their non-crystalline compounds and 59 % in predicting crystallization failure. Their dimensionality reduction via t-SNE reveals their distinct clustering patterns to underscore their complex interplay between molecular structure and crystal propensity. Their experimental validation also corroborates the current findings to demonstrate their efficacy to streamline their crystal engineering for pharmaceutical formulation-based workflows. Notably, the number of rotatable bonds and molecular connectivity index (χov) emerges as pivotal descriptors for enabling their accurate classification with minimal input features. This study elucidates its quantitative structure-crystallinity relationship to provide a valuable tool for crystal design and optimization.
Benzothiophene semiconductor polymer design by machine learning with low exciton binding energy: A vast chemical space generation for new structures
(Elsevier Ltd, 2025) Mallah, Shaimaa H.; Güleryüz, Cihat; Sumrra, Sajjad H.; Hassan, Abrar U.; Güleryüz, Hasan; Mohyuddin, Ayesha; Kyhoiesh, Hussein A.K.
The development of new organic semiconductors with low exciton binding energies (Eb) is crucial for improving the efficiency of organic photovoltaic (PV) devices. Here, we report the generation of a chemical space of benzothiophene (BDT)-based organic semiconductors with lowest Eb energies using machine learning (ML). Our study involves the design of over 500 organic semiconductor structures with low Eb energies and their synthetic accessibility scores. For this, we collect 1061 BDT based compounds from literature, calculated their Eb energies, and predicted them using ML with Random Forest (RF) regression, yielding the best results. Our analysis, using SHAP values, reveals that heavy atoms are the main factors in lowering Eb values. Furthermore, we tested new organic chromophore structures, which showed an efficient shift of their molecular charges. The UV–Vis spectra of these structures exhibits a redshift in the range of 358–667 nm, while their open-circuit voltage (Voc) and light-harvesting efficiency (LHE) ranges from 1.64 to 1.954 V and 52–91 %, respectively. Current study provides a valuable chemical space for the development of new organic semiconductors with improved efficiency. © 2025 Elsevier Ltd
A machine learning and data-oriented quest to screen the degree of long-range order/disorder in polymeric materials
(Elsevier, 2025) Guleryuz, Cihat; Sumrra, Sajjad H.; Hassan, Abrar U.; Mohyuddin, Ayesha; Mahmoud, Mohamed. H. H.
In the realm of polymeric materials, the delicate balance between long-range order and disorder dictates crystal properties, influencing their performance in various applications. To unravel this enigma, we embarked on a machine learning (ML) and data-driven quest, compiling 2500 data points from literature. By harnessing the power of Support Vector Machines (SVM) and Radial Basis Functions (RBF), we trained our model to decipher the intricate relationships between molecular descriptors and crystal properties. Introducing a novel pass/fail system, we screened polymers based on their calculated descriptors, revealing that combining multiple descriptors significantly enhances model performance. Identifying 1200 polymers that failed to meet crystallization requirements provides valuable insights for designing materials with tailored structural features. This groundbreaking study pioneers a data-oriented approach to understanding polymeric materials, paving the way for the creation of novel crystals with optimized properties. By uncovering the hidden patterns of order and disorder, we unlock the secrets of polymeric materials, revolutionizing their applications in various fields.
One step synthesis of tryptophan-isatin carbon nano dots and bio-applications as multifunctional nanoplatforms
(2025) Tok, Kerem; Barlas, F. Barış; Bayır, Ece; Şenışık, Ahmet Murat; Zihnioğlu, Figen; Timur, Suna
The development of natural molecule-derived carbon nano dots (CNDs) marks a significant advancement in biocompatible and sustainable nanomaterials. Tryptophan, capable of crossing the blood-brain barrier (BBB), serves as a precursor to numerous pharmacologically active compounds, while isatin and its derivatives have demonstrated anti-tumor effects, including against brain cancers. This study aimed to synthesize fluorescent CNDs from tryptophan-isatin hybrid precursor and explore their applications in glioblastoma treatment. These CNDs were characterized using techniques such as TEM, SEM-EDS, FTIR, XPS, Raman spectroscopy and UV-Vis spectrophotometry. In vitro tests using the U-87 glioblastoma cell line evaluated cell viability, affinity, and BBB permeability. The CNDs, between 4 and 7 nm in size, exhibited blue and green fluorescence, with no cytotoxic effects observed at concentrations up to 25 µg/mL. The highest BBB permeability rate was determined as 4.3 × 10⁻⁵ cm/s. Additionally, the CNDs demonstrated radiotherapeutic properties, leading to a 51 % reduction in cell viability. This research contributes to nanomedicine by introducing a novel biocompatible material with potential for targeted brain cancer imaging and therapy, while also suggesting broader applications beyond glioblastoma.
On the quest for solar energy harvesters and nonlinear optics: a DFT exploration of A-D-D-A framework with varying sp2 hybridization
(Springer, 2025) Güleryüz, Cihat; Rehman, Muhammad M. U.; Hassan, Abrar U.; Abass, Zainab A.; Mohyuddin, Ayesha; Zafar, Muddassar; Alotaibi, Mohammed T.
In response to address the constraints of fullerene analogues, scientists are constantly working on developing low-cost fullerene-free functionalization for nanoscale organic photovoltaics. During the present study, the computational design and analysis of 14 new non-fullerene dyes (IDIC-O-1 to IDIC-O-14) centered on indacenodithiophene (IDIC) core are proposed with sp2-hybridized nitrogen at varying positions. Regarding their UV–visible assessment, several long-range and range-separated functionals like B3LYP, CAM-B3LYP, ωB97XD, and APFD using the 6-311G + (d,p) basis set have been employed to identify their optimal level of density functional theory (DFT) with an impressive correlation at the CAM-B3LYP level. Their global hardness (η) and global electrophilicity (ω) natures show their persistent nature. The energy gaps (Egaps) are lesser than IDIC and IDIC-O to imply an easier electronic transition. When contrasted to the IDIC-O, the findings indicate that its broad absorption spectrum had a redshift. The efficient HOMO → LUMO-based CT was investigated, and an open-circuit voltage (Voc) study is done on HOMOIDIC → LUMOAcceptor. All dyes have their Voc values lower than reference (IDIC-O) except IDIC-O-11 with a positive value. These lower reorganization energies (RE) for holes and electrons indicate a greater charge transfer (CT). When contrasted to the IDIC-O, the newly designed dyes have better characteristics for solar cell performance.
Exploring structural basis of photovoltaic dye materials to tune power conversion efficiencies: A DFT and ML analysis of Violanthrone
(Elsevier Ltd, 2025) Sumrra, Sajjad H.; Güleryüz, Cihat; Hassan, Abrar U.; Abass, Zainab A.; Hanoon, Talib M.; Mohyuddin, Ayesha; Kyhoiesh, Hussein A.K.; Alotaibi, Mohammed T.
This study employs a systematic approach to modify Violanthrone (V) structures and analyze their impact on photovoltaic (PV) properties. We use cheminformatics based Python library based RDKit tool to calculate their structural descriptors for to correlate them with their PV parameters. Our analysis reveals a positive correlation for their Open-Circuit Voltage (Voc) and Fill Factor (FF) for indicating that their higher voltage output is associated for their efficient charge carrier mobilities. We also predict their Power Conversion Efficiency (PCE) by drawing their their Scharber diagram which achieves their promising efficiency of up to 15 %. To further enhance the reliability our work, we conduct an extensive literature survey of such organic materials to predict their PCEs by their Machine Learning (ML) after utilizing various ML models. Among five tested ML models, it identifies the Random Forecast (RF) model and Gradient Boosting (GB) models as as the optimal one (R-squared value: 0.82). Their feature importance reveals that their FF is the most significant feature to impact their PCEs (importance value: 10.9). Furthermore, we observe a negative correlation between orbital interaction strength (E(2)) values and orbital energy differences E(j)-E(i) which indicates that their stronger orbital interactions are associated with their smaller energy differences. Our study provides valuable insights for their structural basis to PV material designs for enabling their design for efficient materials in energy conversion.
A machine learning and DFT assisted analysis of benzodithiophene based organic dyes for possible photovoltaic applications
(Elsevier B.V., 2025) Güleryüz, Cihat; Sumrra, Sajjad H.; Hassan, Abrar U.; Mohyuddin, Ayesha; Waheeb, Azal S.; Awad, Masar A.; Jalfan, Ayad R.; Noreen, Sadaf; Kyhoiesh, Hussein A.K.; El Azab, Islam H.
We present a synergistic approach to combine Machine Learning (ML), Density Functional Theory (DFT), and molecular descriptor analysis for designing high-performance benzodithiophene (BDT) based chromophores. A dataset of 366 BDT incorporated moieties is compiled from literature while their molecular descriptors are designed by using Python programming language. Linear and Random Forest Regression models produces best results to predict their exciton binding energy (Eb) with their R-Squared (R2) value 0.87 and 0.94 respectively. Their DFT calculations provides additional features, including molecular charges. Their ML models also reveals that their Eb values are a crucial predictor for their photovoltaic (PV) performance as its lower value could facilitate efficient charge carrier separation. For this, their hydrogen bond acceptors (HBA) and topological polar surface area (TPSA) emerges as key descriptors during their regression analysis. Their DFT validation shows negligible differences in their molecular charges to suggest their electron donor/acceptor moieties can significantly impact their chromophore nature. The current research work is helpful for efficiently screening the suitability of organic chromophores for their PV applications through advanced computational tools.
Evaluating the electronic and structural basis of carbon selenide-based quantum dots as photovoltaic design materials : A DFT and ML analysis
(Elsevier Ltd, 2024) Kadhum, Afaf M.; Waheeb, Azal S.; Awad, Masar A.; Hassan, Abrar U.; Sumrra, Sajjad H.; Güleryüz, Cihat; Mohyuddin, Ayesha; Noreen, Sadaf; Kyhoiesh, Hussein A.K.; Alotaibi, Mohammed T.
We present a new study on the design, discovery and space generation of carbon selenide based photovoltaic (PV) materials. By extending acceptors and leveraging density functional theory (DFT) and machine learning (ML) analysis, we discover new QDs with remarkable PV properties. We employ various ML models, to correlate the exciton binding energy (Eb) of 938 relevant compounds from literature with their molecular descriptors of structural features that influence their performance. Our study demonstrates the potential of ML approaches in streamlining the design and discovery of high-efficiency PV materials. Also the RDKit computed molecular descriptors correlates with PV parameters revealed maximum absorption (λmax) ranges of 509–531 nm, light harvesting efficiency (LHE) above 92 %, Open Circuit Voltage (Voc) of 0.22–0.45 V, and short Circuit (Jsc) currents of 37.92–42.75 mA/cm2. Their Predicted Power Conversion Efficiencies (PCE) using the Scharber method reaches upto 09–13 %. This study can pave the way for molecular descriptor-based design of new PV materials, promising a paradigm shift in the development of high-efficiency solar energy conversion technologies.
Molecular engineering on tyrian puprle natural dye as TiO2 based fined tuned photovoltaic dye material: DFT molecular analysis
(2024) Güleryüz, Cihat; Hasan, Duha M.; Awad, Masar A.; Waheeb, Azal S.; Hassan, Abarar U.; Mohyuddin, Ayesha; Kyhoiesh, Hussein A. K.; Alotaibi, Mohammed T.
In this research, molecular modification is employed to see the enhancement in the efficiency of Tyrian Purple (TP), a natural dye, for organic photovoltaic materials. By using Density Functional Theory (DFT) based molecular modeling, seven new structures are designed with pi spacer to extend electron donor moieties. Teheir Frontier Molecular Orbital (FMO) analysis demonstartes their charges with a similar pattern of distributions over their Highest Occupied and Lowed Unocuupied Molecular Orbitals (HOMO/lUMO). This analysls also show their energy gaps (Egaps) to range around 2.97-3.02 eV. Their maximum absorption wavelength (λmax) demosntartes 486-490 nm range to indicate their tendency of absorbing light efficiently. Their Transition Density Matrix (TDM) analysis also reveals their facile electronic transitions without a significant charges over spacers. From calculating their photovoltaic paramters, their Light Harvesting Efficiency (LHE) reaches to 72.4-95.5 %. Also their Open Circuit Voltage (Voc) varies across 1.16-1.34 V. It is found that dyes actively adsorb onto TiO2 clusters to demonstrate their promise for tuning their Conduction Band (CB). This research is an effort for to evaluate the structural correlations to the develop photovoltaic materials through molecular-level design and optimization.
Theoretical calculations of nonlinear optical responses for interpreting nonconjugated molecular systems to affect non-optimal properties
(2024) Güleryüz, Cihat; Sumrra, Sajjad H.; Mohyuddin, Ayesha; Hassan, Abrar U.; Dahshan, Alaa
Understanding the intricacies of polymorphic origins in nonconjugated crystal systems is crucial for optimizing their properties. This study focuses on the crystal growth, characterization, and nonlinear optical (NLO) responses of a system analyzed using single crystal X-ray analysis, revealing a monoclinic geometry. Hirschfeld surface analysis emphasized the significance of intermolecular interactions in driving polymorph development, shedding light on the structural nuances influencing the material's properties. Through the evaluation of density functional theory parameters, the research found that the NLO responses of the system were as efficient as those of widely recognized materials like urea and KDP. Moreover, the stability of the system was confirmed through (NBO) analysis, showcasing its potential for practical applications. By manipulating the polymorphic crystal forms, researchers can potentially unveil new crystalline materials with tailored properties suitable for applications in optical and optoelectronic devices. This work underscores the importance of exploring novel crystal engineering strategies to harness the full potential of materials in the realm of advanced technologies.
Transfer and persistence of microbiota markers from the human hand to the knife: A preliminary study
(2024) Karadayı, Şükriye; Yılmaz, İlknur; Özbek, Tülin; Karadayı, Beytullah
New scientific techniques and methods are always needed to link the perpetrators to the incident or the crime scene. Recent microbiota studies based on NGS (Next-generation sequencing) show that various biological samples from crime scenes have the potential to be used in forensic investigations. Especially when DNA traces belonging to more than one person are insufficient to fully determine the genetic profile, a secret sample, such as a microbiota sample created by the suspect's touch, can be used. In this preliminary study, a fictionalized experimental model was designed to investigate the transfer and persistence of the hand microbiome on the knife handle, which has a high potential to be used in criminal incidents, by metagenomic analysis methods. In addition, it was aimed to determine the transfer of specific bacterial species identified only to the person among the five participants onto the knife handle and their persistence over time. In the first stage of the research, samples were collected from the hands of 5 volunteer participants using the swap method, including their palms. Then, after each participant held a different knife, samples were collected from the knife handles via swabs from different angles of the knives at 4 and 24 h and analyzed by metagenomic methods. The findings of this preliminary study showed that the heatmap graphs generated after UniFrac distance analysis were not successful in establishing any similarity between the hand samples and the post-transfer knife handle samples. Nonetheless, it was observed that the transfer of bacterial species detected in the hand samples to knives differed according to the individuals and some bacterial species were transferred to the knife samples held by the participants. The number of bacterial species detected that are specific to each participant's hand sample was 302 in total, and it was determined that a total of 8.28 % of these bacterial species were transferred to the knife handle samples of the 4th hour and 6.95 % to the knife samples of the 24th hour. In the presented study, considering the transfer of some bacterial species in the hand microbiome, which are effective in the variation between individuals, onto the knife; It has been evaluated that some rare bacterial species can be important potential markers to associate the object with the perpetrator.

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