arXiv subjects

Noor Titan Putri Hartono

Publications and source records attributed to Noor Titan Putri Hartono.

5 recordsLinked to original sources

arXiv2026

Automated Extraction of Techno-Economic Data from 76,000 Energy System Studies

Energy system models guide societally important decisions, but their credibility rests on quantitative assumptions that are difficult to source and audit. Meta-analyses can improve transparency and modeling practices, but the rapid growth of publications makes manual information extraction increasingly impractical. Consequently, databases are updated infrequently and efforts are often duplicated across research groups. Here, we demonstrate the highly accurate automated extraction of quantitative information from 76,000 energy system studies published since 2010. We compile 3.2 million structured quantitative data points together with 20 million associated metadata entries, spanning a broad spectrum of technologies, methodological approaches and system characteristics. Beyond providing input data for models, the resulting FAIR database make the energy systems literature itself analysable. We show where academic assumptions diverge from empirical observed data, and how research priorities vary at scale across technologies, regions and time. To facilitate broad use within the community, the database is provided through an interactive dashboard, enabling users to filter, analyse and download data according to their specific research needs.

cs.CL↗

arXiv2025

Photovoltaic potential of tin perovskites revealed through layer-by-layer investigation of optoelectronic and charge transport properties

Tin perovskites are the most promising environmentally friendly alternative to lead perovskites. Among tin perovskites, FASnI3 (CH4N2SnI3) shows optimum band gap, and easy processability. However, the performance of FASnI3 based solar cells is incomparable to lead perovskites for several reasons, including energy band mismatch between the perovskite absorber film and the charge transporting layers (CTLs) for both types of carriers, i.e., for electrons (ETLs) and holes (HTLs). However, the band diagrams in the literature are inconsistent, and the charge extraction dynamics are poorly understood. In this paper, we study the energy band positions of FASnI3 based perovskites using Kelvin probe (KP) and photoelectron yield spectroscopy (PYS) to provide a precise band diagram of the most used device stack. In addition, we analyze the defects within the current energetic landscape of tin perovskites. We uncover the role of bathocuproine (BCP) in enhancing the electron extraction at the fullerene C60/BCP interface. Furthermore, we used transient surface photovoltage (tr-SPV) for the first time for tin perovskites to understand the charge extraction dynamics of the most reported HTLs such as NiOx and PEDOT, and ETLs such as C60, ICBA, and PCBM. Finally, we used Hall effect, KP, and time-resolved photoluminescence (TRPL) to estimate an accurate value of the p-doping concentration in FASnI3 and showed a consistent result of 1.5 * 1017 cm-3. Our findings prove that the energetic system of tin halide perovskites is deformed and should be redesigned independently from lead perovskites to unlock the full potential of tin perovskites.

cond-mat.mtrl-sci↗

arXiv2022

Processing Induced Distinct Charge Carrier Dynamics of Bulky Organic Halide Treated Perovskites

State-of-the-art metal halide perovskite-based photovoltaics often employ organic ammonium salts, AX, as a surface passivator, where A is a large organic cation and X is a halide. These surface treatments passivate the perovskite by forming layered perovskites (e.g., A2PbX4) or by AX itself serving as a surface passivation agent on the perovskite photoactive film. It remains unclear whether layered perovskites or AX is the ideal passivator due to an incomplete understanding of the interfacial impact and resulting photoexcited carrier dynamics of AX treatment. In the present study, we use TRPL measurements to selectively probe the different interfaces of glass/perovskite/AX to demonstrate the vastly distinct interfacial photoexcited state dynamics with the presence of A2PbX4 or AX. Coupling the TRPL results with X-ray diffraction and nanoscale microscopy measurements, we find that the presence of AX not only passivates the traps at the surface and the grain boundaries, but also induces an α/δ-FAPbI3 phase mixing that alters the carrier dynamics near the glass/perovskite interface and enhances the photoluminescence quantum yield. In contrast, the passivation with A2PbI4 is mostly localized to the surface and grain boundaries near the top surface where the availability of PbI2 directly determines the formation of A2PbI4. Such distinct mechanisms significantly impact the corresponding solar cell performance, and we find AX passivation that has not been converted to a layered perovskite allows for a much larger processing window (e.g., larger allowed variance of AX concentration which is critical for improving the eventual manufacturing yield) and more reproducible condition to realize device performance improvements, while A2PbI4 as a passivator yields a much narrower processing window. We expect these results to enable a more rational route for developing AX for perovskite.

cond-mat.mtrl-sci↗

arXiv2021

Predicting antimicrobial activity of conjugated oligoelectrolyte molecules via machine learning

New antibiotics are needed to battle growing antibiotic resistance, but the development process from hit, to lead, and ultimately to a useful drug, takes decades. Although progress in molecular property prediction using machine-learning methods has opened up new pathways for aiding the antibiotics development process, many existing solutions rely on large datasets and finding structural similarities to existing antibiotics. Challenges remain in modelling of unconventional antibiotics classes that are drawing increasing research attention. In response, we developed an antimicrobial activity prediction model for conjugated oligoelectrolyte molecules, a new class of antibiotics that lacks extensive prior structure-activity relationship studies. Our approach enables us to predict minimum inhibitory concentration for E. coli K12, with 21 molecular descriptors selected by recursive elimination from a set of 5,305 descriptors. This predictive model achieves an R2 of 0.65 with no prior knowledge of the underlying mechanism. We find the molecular representation optimum for the domain is the key to good predictions of antimicrobial activity. In the case of conjugated oligoelectrolytes, a representation reflecting the 3-dimensional shape of the molecules is most critical. Although it is demonstrated with a specific example of conjugated oligoelectrolytes, our proposed approach for creating the predictive model can be readily adapted to other novel antibiotic candidate domains.

physics.app-ph↗

arXiv2019

Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine-learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns. We overcome the scarce-data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data. As a test case, 115 thin-film metal halides spanning 3 dimensionalities and 7 space-groups are synthesized and classified. After testing various algorithms, we develop and implement an all convolutional neural network, with cross validated accuracies for dimensionality and space-group classification of 93% and 89%, respectively. We propose average class activation maps, computed from a global average pooling layer, to allow high model interpretability by human experimentalists, elucidating the root causes of misclassification. Finally, we systematically evaluate the maximum XRD pattern step size (data acquisition rate) before loss of predictive accuracy occurs, and determine it to be 0.16°, which enables an XRD pattern to be obtained and classified in 5.5 minutes or less.

physics.data-an↗