Photo-degradation of Surfactant Compounds Using Uv Rays with Addition of Tio2 Catalysts in Laundry Waste

The utility of synthetic cleaners or commonly called as detergents have developed far indeed in this century. The effective and efficient use of detergents in cleaning process, was attracted to be used in laundry\u27s industry commercially, though, it has some drawback to the society and environments. Since the active materials in the compounds can cause irritating to the skins, cataracts to mature eyes and some other environmental issues due to the excess of foams can prevent oxygen dilution in the water so it be hazardous to water living organisms. Once the promising method to solve this problem is by photo-degradation using photo-catalyst. Photo-catalyst is the process using light and catalyst in order to accelerate chemical transformation, thus, solar or uv-rays can be used as light sources. This research aims at to show the ability of catalyst TiO2 to degrade active material in linear alkyl benzene sulphonate (LAS) and alkyl benzene sulphonate (ABS) which is available in commercial detergent. Photo-degradation of LAS and ABS compounds was determined by optimum capacity TiO2 and irradiance time. Whereas, for environmental application, the degrade compounds such Rinso® and Wipol® was chosen since it is commonly used in daily life. Beside, artificial water waste and laundry water was chosen to show the environmental effect. The result shows that the optimum number of catalyst TiO2 addition for LAS degradation was 1.5 g, and optimum time was 120 minute. Whereas the optimum number of catalyst TiO2 addition for ABS degradation was 2.0 g, and optimum time was 120 minute as well. Thus, for detergent Rinso®, at optimum catalyst addition, we found degradation number was 75.83%, and for detergent Wipol®, at optimum catalyst addition, we found degradation number was 57.94%. Whereas, for artificial waste at optimum catalyst addition, we found degradation number was 91.32%, and for laundry water waste at optimum catalyst addition, we found degradation number was 70.27%

How to Predict the Blooming of the Giant Corpse Inflorescence Amorphophallus Titanum (Becc.) Becc. Ex Arcang [Prediksi Mekarnya Bunga Bangkai Raksasa Amorphophallus Titanum (Becc.) Becc. Ex Arcang]

The giant corpse inflorescence (Amorphophallus titanum) is native to Indonesia. It is one of the flagships of The Center for Plant Conservation Bogor Botanic Gardens, Indonesian Institute of Sciences (LIPI) to raise public awareness for saving plants from extinction and caring the living environment. The blooming of the giant inflorescence attracts many visitors. Therefore, one of the research focuses of Amorphophallus titanum is how to predict the blooming-time in order to inform visitors earlier. The results of this study can be used as the basic information to predict the blooming of the inflorescences. Previous researchers had tried to predict the blooming based on firstly, the differential diagnostics of an inflorescence bud and leaf bud, and secondly, the growth pattern of the flowering bud from one individual sample only; whereas, our results suggested new findings. In this research, the prediction was examined from 2011-2012 on five individual samples planted in Bogor Botanic Gardens. We suggested that (1) the differential diagnostics of an inflorescence bud and leaf bud were not only based on the early bud shapes but also the growth rate, and (2) our results supported Lobin's findings by providing a statistical formula for growth pattern of the flowering bud

Viability Tests On The Seeds Of Rafflesia Arnoldii R.br. And R. Patma Blume

Rafflesia is a holoparasite that is facing extinction in nature. The Center for Plant Conservation Bogor Botanic Gardens LIPI has been pioneering ex situ conservation using grafting methods. However, ex situ conservation using seed innoculation has been a challenge. Studies on the viability testing of Rafflesia seeds aims to provide viable seeds for supporting the ex situ conservation programs based on seed innoculation methods. In particular, the aims of our recent research were: (1) to investigate the morphological characters of the seeds; (2) to determine a method for viability testing the seeds. The seeds of R. arnoldii and R. patma were used in this research. Firstly, the morphological characters of the seeds were observed under a binocular microscope. The viability test using bioassay procedures was as follows: (1) reagent preparation; (2) pre-staining; (3) embryo exposure; (4) staining, and (5) assessment. The research resulted in: (1) morphological description of the characteristics of Rafflesia seeds, and (2) techniques for bioassay viability testing of Rafflesia seeds

Perencanaan Long Storage Jetis Kec Blora Kab Blora

Floods are natural disasters that regularly occur in DAS Lusi during the rainy season. Reduced water catchment areas in the upstream area of the river is one of the causes of increased flood discharge in river Lusi. Attempts to reduce it is to plan long storage system, a building that utilizes water as a resevoir riverwith weir building in the downstream.Long storage will be built on the upstream of the Lusi, located at Jetis, Kecamatan Blora, Kabupaten Blora. The purpose of the construction of long storage Jetis is a flood control system and as a resevoir for the surrounding areas. From the result of rainfall data in Blora, Jiken, Jepon, Bogorejo and Tempuran station, return period of 25 years Q25 = 242.059 m3 / sec are used to analyze the capacity of the river and weir stability. While the discharge is used in analyzing the resevoir using a debit andalan based formula FJ Mock with large long storage bin is 42 liters / sec.Plan development time required was 12 weeks with a total budget of Rp. 12,852,198,000.00. (Twelve billion eight hundred and fifty-two million one hundred ninety eight thousand rupiah)

The Effect of Carbon Nanotube Composite Addition on Biomass-Based Supercapacitor

Electric vehicles are set to become a most attractive alternative transportation mode due to their high efficiency and low emission. Electric vehicles require an efficient energy storage system, e.g. a supercapacitor. Coconut shells have high lignocellulosic content and are not being fully utilized in Indonesia. The lignocellulose could be converted into activated carbon for use as the electrode on a hybrid supercapacitor. This research focused on studying the effect of the addition of carbon nanotube (CNT) composite to porous graphene-like nanosheets (PGNS) as the electrode on a hybrid supercapacitor. The PGNS and CNT composite were synthesized via simultaneous activation and carbonization. Nickel oxide was used as the counter electrode. The CNT composite had a large surface area of 1374.8 m2g-1, pore volume of 1.1 cm3g, and pore size of 3.2 nm. On the other hand, the PGNS had a surface area of 666.1 m2g-1, pore volume of 0.47 cm3g, and pore size of 2.8 nm. The electrode pair between the NiO and the activated carbon achieved 5.69 F/g and 94.1% cycle durability after 10 charging and discharging cycles. The composite had an energy density of 0.38 W h kg-1. The aim of this research was to provide an alternative formula for producing high-performance supercapacitor materials

A nonlinear updating algorithm captures suboptimal inference in the presence of signal-dependent noise

Bayesian models have advanced the idea that humans combine prior beliefs and sensory observations to optimize behavior. How the brain implements Bayes-optimal inference, however, remains poorly understood. Simple behavioral tasks suggest that the brain can flexibly represent probability distributions. An alternative view is that the brain relies on simple algorithms that can implement Bayes-optimal behavior only when the computational demands are low. To distinguish between these alternatives, we devised a task in which Bayes-optimal performance could not be matched by simple algorithms. We asked subjects to estimate and reproduce a time interval by combining prior information with one or two sequential measurements. In the domain of time, measurement noise increases with duration. This property takes the integration of multiple measurements beyond the reach of simple algorithms. We found that subjects were able to update their estimates using the second measurement but their performance was suboptimal, suggesting that they were unable to update full probability distributions. Instead, subjects’ behavior was consistent with an algorithm that predicts upcoming sensory signals, and applies a nonlinear function to errors in prediction to update estimates. These results indicate that the inference strategies employed by humans may deviate from Bayes-optimal integration when the computational demands are high